Datasets:
Sculpt-AI
/
GIMBench-sci-erc

Dataset card Data Studio Files
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IJCAI_2001_5_abs
[ [ "Helping", "end", "users", "build", "and", "check", "process", "models", "is", "a", "challenge", "for", "many", "science", "and", "engineering", "fields.", "Many", "AI", "researchers", "have", "investigated", "useful", ...
[ "Helping", "end", "users", "build", "and", "check", "process", "models", "is", "a", "challenge", "for", "many", "science", "and", "engineering", "fields.", "Many", "AI", "researchers", "have", "investigated", "useful", "ways", "of", "verifying", "and", "validati...
Helping end users build and check process models is a challenge for many science and engineering fields. Many AI researchers have investigated useful ways of verifying and validating knowledge bases for ontologies and rules, but it is not easy to directly apply them to checking process models. Other techniques develope...
[ { "end": 48, "id": "T1", "label": "Method", "start": 34, "text": "process models" }, { "end": 103, "id": "T2", "label": "Task", "start": 73, "text": "science and engineering fields" }, { "end": 198, "id": "T3", "label": "Material", "start": 183, "t...
[ { "head": "checking and refining planning knowledge", "head_span": [ 326, 366 ], "relation": "USED-FOR", "tail": "automated plan generation", "tail_span": [ 384, 409 ] }, { "head": "complementary approach", "head_span": [ 492, 514 ], ...
[ [ "complementary approach", "system", "KANAL", "It", "KANAL" ], [ "interdepen-dency models", "them" ], [ "process models", "process models", "process models", "process models" ] ]
IJCAI_2003_15_abs
[ [ "Many", "description", "logics", "(DLs)", "combine", "knowledge", "representation", "on", "an", "abstract,", "logical", "level", "with", "an", "interface", "to", "\"concrete\"", "domains", "such", "as", "numbers", "and", "...
[ "Many", "description", "logics", "(DLs)", "combine", "knowledge", "representation", "on", "an", "abstract,", "logical", "level", "with", "an", "interface", "to", "\"concrete\"", "domains", "such", "as", "numbers", "and", "strings.", "We", "propose", "to", "extend"...
Many description logics (DLs) combine knowledge representation on an abstract, logical level with an interface to "concrete" domains such as numbers and strings. We propose to extend such DLs with key constraints that allow the expression of statements like "US citizens are uniquely identified by their social security ...
[ { "end": 29, "id": "T1", "label": "Method", "start": 5, "text": "description logics (DLs)" }, { "end": 62, "id": "T2", "label": "Method", "start": 38, "text": "knowledge representation" }, { "end": 191, "id": "T3", "label": "Method", "start": 188, ...
[ { "head": "knowledge representation", "head_span": [ 38, 62 ], "relation": "CONJUNCTION", "tail": "description logics (DLs)", "tail_span": [ 5, 29 ] } ]
[ [ "description logics (DLs)", "DLs" ] ]
IJCAI_2010_4_abs
[ [ "In", "Bayesian", "machine", "learning,", "conjugate", "priors", "are", "popular,", "mostly", "due", "to", "mathematical", "convenience.", "In", "this", "paper,", "we", "show", "that", "there", "are", "deeper", "reasons", ...
[ "In", "Bayesian", "machine", "learning,", "conjugate", "priors", "are", "popular,", "mostly", "due", "to", "mathematical", "convenience.", "In", "this", "paper,", "we", "show", "that", "there", "are", "deeper", "reasons", "for", "choosing", "a", "conjugate", "pr...
In Bayesian machine learning, conjugate priors are popular, mostly due to mathematical convenience. In this paper, we show that there are deeper reasons for choosing a conjugate prior. Specifically, we formulate the conjugate prior in the form of Bregman divergence and show that it is the inherent geometry of conjugate...
[ { "end": 28, "id": "T1", "label": "Task", "start": 3, "text": "Bayesian machine learning" }, { "end": 46, "id": "T2", "label": "OtherScientificTerm", "start": 30, "text": "conjugate priors" }, { "end": 183, "id": "T3", "label": "OtherScientificTerm", "...
[ { "head": "conjugate priors", "head_span": [ 30, 46 ], "relation": "PART-OF", "tail": "Bayesian machine learning", "tail_span": [ 3, 28 ] }, { "head": "generative and discriminative components", "head_span": [ 662, 702 ], "relation"...
[ [ "prior", "conjugate priors" ] ]
IJCAI_2010_5_abs
[ [ "Recent", "advances", "in", "linear", "classification", "have", "shown", "that", "for", "applications", "such", "as", "document", "classification,", "the", "training", "can", "be", "extremely", "efficient.", "However,", "most"...
[ "Recent", "advances", "in", "linear", "classification", "have", "shown", "that", "for", "applications", "such", "as", "document", "classification,", "the", "training", "can", "be", "extremely", "efficient.", "However,", "most", "of", "the", "existing", "training", ...
Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing training methods are designed by assuming that data can be stored in the computer memory. These methods cannot be easily applied to data large...
[ { "end": 40, "id": "T1", "label": "Task", "start": 19, "text": "linear classification" }, { "end": 73, "id": "T2", "label": "Generic", "start": 61, "text": "applications" }, { "end": 105, "id": "T3", "label": "Task", "start": 82, "text": "document ...
[ { "head": "document classification", "head_span": [ 82, 105 ], "relation": "HYPONYM-OF", "tail": "applications", "tail_span": [ 61, 73 ] }, { "head": "block minimization framework", "head_span": [ 410, 438 ], "relation": "USED-FOR",...
[ [ "training methods", "methods" ], [ "block minimization framework", "framework", "method" ] ]
IJCAI_2013_15_abs
[ [ "The", "Interval", "Algebra", "(IA)", "and", "a", "subset", "of", "the", "Region", "Connection", "Calculus", "(RCC),", "namely", "RCC-8,", "are", "the", "dominant", "Artificial", "Intelligence", "approaches", "for", "repre...
[ "The", "Interval", "Algebra", "(IA)", "and", "a", "subset", "of", "the", "Region", "Connection", "Calculus", "(RCC),", "namely", "RCC-8,", "are", "the", "dominant", "Artificial", "Intelligence", "approaches", "for", "representing", "and", "reasoning", "about", "qu...
The Interval Algebra (IA) and a subset of the Region Connection Calculus (RCC), namely RCC-8, are the dominant Artificial Intelligence approaches for representing and reasoning about qualitative temporal and topological relations respectively. Such qualitative information can be formulated as a Qualitative Constraint N...
[ { "end": 25, "id": "T1", "label": "Method", "start": 4, "text": "Interval Algebra (IA)" }, { "end": 78, "id": "T2", "label": "Method", "start": 46, "text": "Region Connection Calculus (RCC)" }, { "end": 92, "id": "T3", "label": "Method", "start": 87, ...
[ { "head": "RCC-8", "head_span": [ 87, 92 ], "relation": "HYPONYM-OF", "tail": "Region Connection Calculus (RCC)", "tail_span": [ 46, 78 ] }, { "head": "Artificial Intelligence approaches", "head_span": [ 111, 145 ], "relation": "USE...
[ [ "qualitative temporal and topological relations", "qualitative information" ], [ "QCN", "QCN", "QCNs", "Qualitative Constraint Network (QCN)" ], [ "algorithm", "algorithm", "algorithm", "approach" ], [ "Interval Algebra (IA)", "IA" ], [ "RCC-8", ...
IJCAI_2013_4_abs
[ [ "In", "this", "paper,", "we", "state", "the", "challenges", "of", "high-level", "program", "execution", "in", "multi-agent", "settings.", "We", "first", "introduce", "high-level", "program", "execution", "and", "the", "rel...
[ "In", "this", "paper,", "we", "state", "the", "challenges", "of", "high-level", "program", "execution", "in", "multi-agent", "settings.", "We", "first", "introduce", "high-level", "program", "execution", "and", "the", "related", "work.", "Then", "we", "describe", ...
In this paper, we state the challenges of high-level program execution in multi-agent settings. We first introduce high-level program execution and the related work. Then we describe the completed work, the future work and its approaches. We conclude with the expected contributions of our research.
[ { "end": 70, "id": "T1", "label": "Task", "start": 42, "text": "high-level program execution" }, { "end": 143, "id": "T2", "label": "Task", "start": 115, "text": "high-level program execution" } ]
[]
[ [ "high-level program execution", "high-level program execution" ] ]
IJCAI_2013_5_abs
[ [ "Constraint", "propagation", "is", "one", "of", "the", "key", "techniques", "in", "constraint", "programming,", "and", "a", "large", "body", "of", "work", "has", "built", "up", "around", "it.", "Special-purpose", "cons...
[ "Constraint", "propagation", "is", "one", "of", "the", "key", "techniques", "in", "constraint", "programming,", "and", "a", "large", "body", "of", "work", "has", "built", "up", "around", "it.", "Special-purpose", "constraint", "propagation", "algorithms", "frequen...
Constraint propagation is one of the key techniques in constraint programming, and a large body of work has built up around it. Special-purpose constraint propagation algorithms frequently make implicit use of short supports — by examining a subset of the variables, they can infer support (a justification that a variab...
[ { "end": 22, "id": "T1", "label": "Method", "start": 0, "text": "Constraint propagation" }, { "end": 77, "id": "T2", "label": "Method", "start": 55, "text": "constraint programming" }, { "end": 177, "id": "T3", "label": "Method", "start": 128, "tex...
[ { "head": "SHORTSTR2", "head_span": [ 759, 768 ], "relation": "COMPARE", "tail": "SHORTGAC", "tail_span": [ 813, 821 ] }, { "head": "SHORTSTR2", "head_span": [ 759, 768 ], "relation": "COMPARE", "tail": "HAGGISGAC", "tail_sp...
[ [ "SHORTSTR2", "SHORTSTR2", "SHORTSTR2", "SHORTSTR2" ], [ "STR2+", "STR2+", "Simple Tabular Reduction algorithm STR2+" ] ]
IJCAI_2016_412_abs
[ [ "Along", "with", "the", "increasing", "requirements,", "the", "hash-tag", "recommendation", "task", "for", "microblogs", "has", "been", "receiving", "considerable", "attention", "in", "recent", "years.", "Various", "researchers", ...
[ "Along", "with", "the", "increasing", "requirements,", "the", "hash-tag", "recommendation", "task", "for", "microblogs", "has", "been", "receiving", "considerable", "attention", "in", "recent", "years.", "Various", "researchers", "have", "studied", "the", "problem", ...
Along with the increasing requirements, the hash-tag recommendation task for microblogs has been receiving considerable attention in recent years. Various researchers have studied the problem from different aspects. However, most of these methods usually need handcrafted features. Motivated by the successful use of con...
[ { "end": 72, "id": "T1", "label": "Task", "start": 44, "text": "hash-tag recommendation task" }, { "end": 87, "id": "T2", "label": "Material", "start": 77, "text": "microblogs" }, { "end": 280, "id": "T3", "label": "OtherScientificTerm", "start": 260, ...
[ { "head": "hash-tag recommendation task", "head_span": [ 44, 72 ], "relation": "USED-FOR", "tail": "microblogs", "tail_span": [ 77, 87 ] }, { "head": "F1-score", "head_span": [ 972, 980 ], "relation": "EVALUATE-FOR", "tail": "st...
[ [ "state-of-the-art method", "convolutional neural networks (CNNs)", "CNNs" ], [ "model", "method", "architecture" ], [ "hash-tag recommendation task", "hashtag recommendation problem" ] ]
IJCAI_2016_413_abs
[ [ "In", "some", "auction", "domains,", "there", "is", "uncertainty", "regarding", "the", "final", "availability", "of", "the", "goods", "being", "auctioned", "off.", "For", "example,", "a", "government", "may", "auction", ...
[ "In", "some", "auction", "domains,", "there", "is", "uncertainty", "regarding", "the", "final", "availability", "of", "the", "goods", "being", "auctioned", "off.", "For", "example,", "a", "government", "may", "auction", "off", "spectrum", "from", "its", "public"...
In some auction domains, there is uncertainty regarding the final availability of the goods being auctioned off. For example, a government may auction off spectrum from its public safety network, but it may need this spectrum back in times of emergency. In such a domain, standard combinatorial auctions perform poorly b...
[ { "end": 23, "id": "T1", "label": "Task", "start": 8, "text": "auction domains" }, { "end": 270, "id": "T2", "label": "Generic", "start": 264, "text": "domain" }, { "end": 303, "id": "T3", "label": "Method", "start": 281, "text": "combinatorial auc...
[ { "head": "design of core-selecting payment rules", "head_span": [ 460, 498 ], "relation": "USED-FOR", "tail": "domains", "tail_span": [ 508, 515 ] }, { "head": "core-selecting rules", "head_span": [ 870, 890 ], "relation": "USED-FO...
[ [ "auction domains", "domain", "domains", "domain" ], [ "combinatorial auctions", "they" ], [ "IR violations", "violations of individual rationality (IR)" ], [ "IR", "individual rationality (IR)" ], [ "core-selecting rules", "rules", "rules" ] ]
IJCAI_2016_420_abs
[ [ "In", "cross-domain", "learning,", "there", "is", "a", "more", "challenging", "problem", "that", "the", "domain", "divergence", "involves", "more", "than", "one", "dominant", "factors,", "e.g.,", "different", "viewpoints", ...
[ "In", "cross-domain", "learning,", "there", "is", "a", "more", "challenging", "problem", "that", "the", "domain", "divergence", "involves", "more", "than", "one", "dominant", "factors,", "e.g.,", "different", "viewpoints", ",", "various", "resolutions", "and", "ch...
In cross-domain learning, there is a more challenging problem that the domain divergence involves more than one dominant factors, e.g., different viewpoints , various resolutions and changing illuminations. Fortunately, an intermediate domain could often be found to build a bridge across them to facilitate the learning...
[ { "end": 24, "id": "T1", "label": "Task", "start": 3, "text": "cross-domain learning" }, { "end": 88, "id": "T2", "label": "OtherScientificTerm", "start": 71, "text": "domain divergence" }, { "end": 128, "id": "T3", "label": "Generic", "start": 112, ...
[ { "head": "viewpoints", "head_span": [ 146, 156 ], "relation": "HYPONYM-OF", "tail": "dominant factors", "tail_span": [ 112, 128 ] }, { "head": "resolutions", "head_span": [ 167, 178 ], "relation": "HYPONYM-OF", "tail": "dominan...
[ [ "cross-domain learning", "cross-domain problem", "learning problem" ], [ "method", "Coupled Marginalized Denoising Auto-encoders framework" ] ]
IJCAI_2016_423_abs
[ [ "Learning", "video", "representation", "is", "not", "a", "trivial", "task,", "as", "video", "is", "an", "information-intensive", "media", "where", "each", "frame", "does", "not", "exist", "independently.", "Locally,", "a",...
[ "Learning", "video", "representation", "is", "not", "a", "trivial", "task,", "as", "video", "is", "an", "information-intensive", "media", "where", "each", "frame", "does", "not", "exist", "independently.", "Locally,", "a", "video", "frame", "is", "visually", "an...
Learning video representation is not a trivial task, as video is an information-intensive media where each frame does not exist independently. Locally, a video frame is visually and semantically similar with its adjacent frames. Holistically, a video has its inherent structure—the correlations among video frames. For e...
[ { "end": 29, "id": "T1", "label": "Task", "start": 0, "text": "Learning video representation" }, { "end": 395, "id": "T2", "label": "OtherScientificTerm", "start": 386, "text": "semantics" }, { "end": 421, "id": "T3", "label": "OtherScientificTerm", "s...
[ { "head": "context information", "head_span": [ 402, 421 ], "relation": "USED-FOR", "tail": "intrinsic representation of a video frame", "tail_span": [ 465, 506 ] }, { "head": "approach", "head_span": [ 542, 550 ], "relation": "USED...
[ [ "approach", "approach" ], [ "deep representations", "learned representation", "deep video representation" ] ]
IJCNLP_2005_3_abs
[ [ "Automatic", "image", "annotation", "is", "a", "newly", "developed", "and", "promising", "technique", "to", "provide", "semantic", "image", "retrieval", "via", "text", "descriptions.", "It", "concerns", "a", "process", "of...
[ "Automatic", "image", "annotation", "is", "a", "newly", "developed", "and", "promising", "technique", "to", "provide", "semantic", "image", "retrieval", "via", "text", "descriptions.", "It", "concerns", "a", "process", "of", "automatically", "labeling", "the", "im...
Automatic image annotation is a newly developed and promising technique to provide semantic image retrieval via text descriptions. It concerns a process of automatically labeling the image contents with a pre-defined set of keywords which are exploited to represent the image semantics. A Maximum Entropy Model-based app...
[ { "end": 26, "id": "T1", "label": "Task", "start": 0, "text": "Automatic image annotation" }, { "end": 107, "id": "T2", "label": "Task", "start": 83, "text": "semantic image retrieval" }, { "end": 129, "id": "T3", "label": "Material", "start": 112, ...
[ { "head": "Maximum Entropy Model-based approach", "head_span": [ 289, 325 ], "relation": "USED-FOR", "tail": "automatic image annotation", "tail_span": [ 341, 367 ] }, { "head": "keywords", "head_span": [ 224, 232 ], "relation": "US...
[ [ "Automatic image annotation", "annotation", "annotation", "automatic image annotation" ], [ "Maximum Entropy Model", "Maximum Entropy Model" ] ]
INTERSPEECH_2000_483_abs
[ [ "Is", "it", "possible", "to", "use", "out-of-domain", "acoustic", "training", "data", "to", "improve", "a", "speech", "recognizer's", "performance", "on", "a", "speciic,", "independent", "application?", "In", "our", "exper...
[ "Is", "it", "possible", "to", "use", "out-of-domain", "acoustic", "training", "data", "to", "improve", "a", "speech", "recognizer's", "performance", "on", "a", "speciic,", "independent", "application?", "In", "our", "experiments,", "we", "use", "Wallstreet", "Jour...
Is it possible to use out-of-domain acoustic training data to improve a speech recognizer's performance on a speciic, independent application? In our experiments, we use Wallstreet Journal (WSJ) data to train a recognizer, which is adapted and evaluated in the Phonebook domain. Apart from their common language (US Engl...
[ { "end": 58, "id": "T1", "label": "Material", "start": 22, "text": "out-of-domain acoustic training data" }, { "end": 89, "id": "T2", "label": "Method", "start": 72, "text": "speech recognizer" }, { "end": 199, "id": "T3", "label": "Material", "start":...
[ { "head": "out-of-domain acoustic training data", "head_span": [ 22, 58 ], "relation": "USED-FOR", "tail": "speech recognizer", "tail_span": [ 72, 89 ] }, { "head": "Wallstreet Journal (WSJ) data", "head_span": [ 170, 199 ], "relati...
[ [ "recognition", "recognition" ], [ "speech recognizer", "recognizer" ] ]
INTERSPEECH_2000_490_abs
[ [ "Currently,", "N-gram", "models", "are", "the", "most", "common", "and", "widely", "used", "models", "for", "statistical", "language", "modeling.", "In", "this", "paper,", "we", "investigated", "an", "alternative", "way", ...
[ "Currently,", "N-gram", "models", "are", "the", "most", "common", "and", "widely", "used", "models", "for", "statistical", "language", "modeling.", "In", "this", "paper,", "we", "investigated", "an", "alternative", "way", "to", "build", "language", "models,", "i...
Currently, N-gram models are the most common and widely used models for statistical language modeling. In this paper, we investigated an alternative way to build language models, i.e., using artificial neural networks to learn the language model. Our experiment result shows that the neural network can learn a language ...
[ { "end": 24, "id": "T1", "label": "Method", "start": 11, "text": "N-gram models" }, { "end": 101, "id": "T2", "label": "Task", "start": 72, "text": "statistical language modeling" }, { "end": 177, "id": "T3", "label": "Method", "start": 162, "text"...
[ { "head": "N-gram models", "head_span": [ 11, 24 ], "relation": "USED-FOR", "tail": "statistical language modeling", "tail_span": [ 72, 101 ] }, { "head": "artificial neural networks", "head_span": [ 191, 217 ], "relation": "USED-FO...
[ [ "language models", "language model" ], [ "artificial neural networks", "neural network" ] ]
INTERSPEECH_2001_21_abs
[ [ "SmartKom", "is", "a", "multimodal", "dialog", "system", "that", "combines", "speech,", "gesture,", "and", "mimics", "input", "and", "output.", "Spontaneous", "speech", "understanding", "is", "combined", "with", "the", "vi...
[ "SmartKom", "is", "a", "multimodal", "dialog", "system", "that", "combines", "speech,", "gesture,", "and", "mimics", "input", "and", "output.", "Spontaneous", "speech", "understanding", "is", "combined", "with", "the", "video-based", "recognition", "of", "natural", ...
SmartKom is a multimodal dialog system that combines speech, gesture, and mimics input and output. Spontaneous speech understanding is combined with the video-based recognition of natural gestures. One of the major scientific goals of SmartKom is to design new computational methods for the seamless integration and mutu...
[ { "end": 8, "id": "T1", "label": "Method", "start": 0, "text": "SmartKom" }, { "end": 38, "id": "T2", "label": "Method", "start": 14, "text": "multimodal dialog system" }, { "end": 59, "id": "T3", "label": "Material", "start": 53, "text": "speech" ...
[ { "head": "SmartKom", "head_span": [ 0, 8 ], "relation": "HYPONYM-OF", "tail": "multimodal dialog system", "tail_span": [ 14, 38 ] }, { "head": "Spontaneous speech understanding", "head_span": [ 99, 131 ], "relation": "CONJUNCTION",...
[ [ "SmartKom", "SmartKom", "SmartKom architecture", "SmartKom demonstrator", "SmartKom" ] ]
INTERSPEECH_2001_28_abs
[ [ "In", "this", "paper", "we", "summarize", "the", "experiences", "gained", "from", "a", "field", "trial", "of", "a", "speaker", "verification", "system.", "In", "the", "test", "implementation", "access", "to", "two", ...
[ "In", "this", "paper", "we", "summarize", "the", "experiences", "gained", "from", "a", "field", "trial", "of", "a", "speaker", "verification", "system.", "In", "the", "test", "implementation", "access", "to", "two", "rooms", "at", "the", "University", "of", ...
In this paper we summarize the experiences gained from a field trial of a speaker verification system. In the test implementation access to two rooms at the University of Frankfurt had been controlled by a speaker verification system. The paper is organized as follows: Firstly, we will describe the system concepts and ...
[ { "end": 101, "id": "T1", "label": "Method", "start": 74, "text": "speaker verification system" }, { "end": 233, "id": "T2", "label": "Method", "start": 206, "text": "speaker verification system" }, { "end": 383, "id": "T3", "label": "Method", "start":...
[]
[ [ "speaker verification system", "speaker verification system" ] ]
INTERSPEECH_2001_31_abs
[ [ "This", "paper", "presents", "a", "method", "for", "blind", "estimation", "of", "reverberation", "times", "in", "reverberant", "enclosures.", "The", "proposed", "algorithm", "is", "based", "on", "a", "statistical", "model"...
[ "This", "paper", "presents", "a", "method", "for", "blind", "estimation", "of", "reverberation", "times", "in", "reverberant", "enclosures.", "The", "proposed", "algorithm", "is", "based", "on", "a", "statistical", "model", "of", "short-term", "log-energy", "seque...
This paper presents a method for blind estimation of reverberation times in reverberant enclosures. The proposed algorithm is based on a statistical model of short-term log-energy sequences for echo-free speech. Given a speech utterance recorded in a reverberant room, it computes a Maximum Likelihood estimate of the ro...
[ { "end": 28, "id": "T1", "label": "Generic", "start": 22, "text": "method" }, { "end": 72, "id": "T2", "label": "Task", "start": 33, "text": "blind estimation of reverberation times" }, { "end": 98, "id": "T3", "label": "OtherScientificTerm", "start": ...
[ { "head": "reverberant enclosures", "head_span": [ 76, 98 ], "relation": "FEATURE-OF", "tail": "blind estimation of reverberation times", "tail_span": [ 33, 72 ] }, { "head": "method", "head_span": [ 22, 28 ], "relation": "USED-FOR"...
[ [ "method", "approach", "algorithm" ], [ "Maximum Likelihood estimate of the room full-band reverberation time", "estimation method", "method" ], [ "robust automatic speech recognition", "application" ] ]
INTERSPEECH_2006_21_abs
[ [ "In", "this", "paper,", "we", "discuss", "language", "model", "adaptation", "methods", "given", "a", "word", "list", "and", "a", "raw", "corpus.", "In", "this", "situation,", "the", "general", "method", "is", "to",...
[ "In", "this", "paper,", "we", "discuss", "language", "model", "adaptation", "methods", "given", "a", "word", "list", "and", "a", "raw", "corpus.", "In", "this", "situation,", "the", "general", "method", "is", "to", "segment", "the", "raw", "corpus", "automat...
In this paper, we discuss language model adaptation methods given a word list and a raw corpus. In this situation, the general method is to segment the raw corpus automatically using a word list, correct the output sentences by hand, and build a model from the segmented corpus. In this sentence-by-sentence error correc...
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[ { "head": "raw corpus", "head_span": [ 84, 94 ], "relation": "USED-FOR", "tail": "language model adaptation methods", "tail_span": [ 26, 59 ] }, { "head": "word list", "head_span": [ 68, 77 ], "relation": "USED-FOR", "tail": "la...
[ [ "language model adaptation methods", "method", "method" ] ]
INTERSPEECH_2006_28_abs
[ [ "We", "investigated", "whether", "automatic", "phonetic", "transcriptions", "(APTs)", "can", "replace", "manually", "verified", "phonetic", "transcriptions", "(MPTs)", "in", "a", "large", "corpus-based", "study", "on", "pronunciat...
[ "We", "investigated", "whether", "automatic", "phonetic", "transcriptions", "(APTs)", "can", "replace", "manually", "verified", "phonetic", "transcriptions", "(MPTs)", "in", "a", "large", "corpus-based", "study", "on", "pronunciation", "variation.", "To", "this", "end...
We investigated whether automatic phonetic transcriptions (APTs) can replace manually verified phonetic transcriptions (MPTs) in a large corpus-based study on pronunciation variation. To this end, we compared the performance of both transcription types in a classification experiment aimed at establishing the direct inf...
[ { "end": 64, "id": "T1", "label": "OtherScientificTerm", "start": 24, "text": "automatic phonetic transcriptions (APTs)" }, { "end": 118, "id": "T2", "label": "OtherScientificTerm", "start": 77, "text": "manually verified phonetic transcriptions" }, { "end": 182, ...
[ { "head": "automatic phonetic transcriptions (APTs)", "head_span": [ 24, 64 ], "relation": "USED-FOR", "tail": "pronunciation variation", "tail_span": [ 159, 182 ] }, { "head": "manually verified phonetic transcriptions", "head_span": [ 77, ...
[ [ "pronunciation variation", "pronunciation variation" ], [ "automatic phonetic transcriptions (APTs)", "APT" ], [ "MPT", "manually verified phonetic transcriptions" ], [ "classifiers", "classifiers" ] ]
INTERSPEECH_2006_31_abs
[ [ "Within", "the", "EU", "Network", "of", "Excellence", "PASCAL,", "a", "challenge", "was", "organized", "to", "design", "a", "statistical", "machine", "learning", "algorithm", "that", "segments", "words", "into", "the", ...
[ "Within", "the", "EU", "Network", "of", "Excellence", "PASCAL,", "a", "challenge", "was", "organized", "to", "design", "a", "statistical", "machine", "learning", "algorithm", "that", "segments", "words", "into", "the", "smallest", "meaning-bearing", "units", "of",...
Within the EU Network of Excellence PASCAL, a challenge was organized to design a statistical machine learning algorithm that segments words into the smallest meaning-bearing units of language , morphemes. Ideally, these are basic vocabulary units suitable for different tasks, such as speech and text understanding, mac...
[ { "end": 120, "id": "T1", "label": "Method", "start": 82, "text": "statistical machine learning algorithm" }, { "end": 192, "id": "T2", "label": "OtherScientificTerm", "start": 150, "text": "smallest meaning-bearing units of language" }, { "end": 204, "id": "T...
[ { "head": "morphemes", "head_span": [ 195, 204 ], "relation": "HYPONYM-OF", "tail": "smallest meaning-bearing units of language", "tail_span": [ 150, 192 ] }, { "head": "statistical machine learning algorithm", "head_span": [ 82, 120 ],...
[ [ "morphemes", "these" ] ]
INTERSPEECH_2007_20_abs
[ [ "We", "propose", "an", "efficient", "dialogue", "management", "for", "an", "information", "navigation", "system", "based", "on", "a", "document", "knowledge", "base.", "It", "is", "expected", "that", "incorporation", "of",...
[ "We", "propose", "an", "efficient", "dialogue", "management", "for", "an", "information", "navigation", "system", "based", "on", "a", "document", "knowledge", "base.", "It", "is", "expected", "that", "incorporation", "of", "appropriate", "N-best", "candidates", "o...
We propose an efficient dialogue management for an information navigation system based on a document knowledge base. It is expected that incorporation of appropriate N-best candidates of ASR and contextual information will improve the system performance. The system also has several choices in generating responses or co...
[ { "end": 43, "id": "T1", "label": "Method", "start": 24, "text": "dialogue management" }, { "end": 80, "id": "T2", "label": "Task", "start": 51, "text": "information navigation system" }, { "end": 115, "id": "T3", "label": "Material", "start": 92, ...
[ { "head": "dialogue management", "head_span": [ 24, 43 ], "relation": "USED-FOR", "tail": "information navigation system", "tail_span": [ 51, 80 ] }, { "head": "document knowledge base", "head_span": [ 92, 115 ], "relation": "USED-F...
[ [ "strategy", "framework", "minimization of Bayes risk" ], [ "spoken dialogue system \" Dialogue Navigator for Kyoto City \"", "information navigation system", "system", "system" ] ]
INTERSPEECH_2007_21_abs
[ [ "Conventional", "HMMs", "have", "weak", "duration", "constraints.", "In", "noisy", "conditions,", "the", "mismatch", "between", "corrupted", "speech", "signals", "and", "models", "trained", "on", "clean", "speech", "may", ...
[ "Conventional", "HMMs", "have", "weak", "duration", "constraints.", "In", "noisy", "conditions,", "the", "mismatch", "between", "corrupted", "speech", "signals", "and", "models", "trained", "on", "clean", "speech", "may", "cause", "the", "decoder", "to", "produce"...
Conventional HMMs have weak duration constraints. In noisy conditions, the mismatch between corrupted speech signals and models trained on clean speech may cause the decoder to produce word matches with unrealistic durations. This paper presents a simple way to incorporate word duration constraints by unrolling HMMs to...
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[ { "head": "weak duration constraints", "head_span": [ 23, 48 ], "relation": "FEATURE-OF", "tail": "HMMs", "tail_span": [ 13, 17 ] }, { "head": "clean speech", "head_span": [ 139, 151 ], "relation": "USED-FOR", "tail": "models", ...
[]
INTERSPEECH_2007_28_abs
[ [ "This", "paper", "presents", "a", "new", "two-pass", "algorithm", "for", "Extra", "Large", "(more", "than", "1M", "words)", "Vocabulary", "COntinuous", "Speech", "recognition", "based", "on", "the", "Information", "Retriev...
[ "This", "paper", "presents", "a", "new", "two-pass", "algorithm", "for", "Extra", "Large", "(more", "than", "1M", "words)", "Vocabulary", "COntinuous", "Speech", "recognition", "based", "on", "the", "Information", "Retrieval", "(ELVIRCOS).", "The", "principle", "o...
This paper presents a new two-pass algorithm for Extra Large (more than 1M words) Vocabulary COntinuous Speech recognition based on the Information Retrieval (ELVIRCOS). The principle of this approach is to decompose a recognition process into two passes where the first pass builds the words subset for the second pass ...
[ { "end": 44, "id": "T1", "label": "Generic", "start": 26, "text": "two-pass algorithm" }, { "end": 122, "id": "T2", "label": "Task", "start": 49, "text": "Extra Large (more than 1M words) Vocabulary COntinuous Speech recognition" }, { "end": 168, "id": "T3", ...
[ { "head": "two-pass algorithm", "head_span": [ 26, 44 ], "relation": "USED-FOR", "tail": "Extra Large (more than 1M words) Vocabulary COntinuous Speech recognition", "tail_span": [ 49, 122 ] }, { "head": "Information Retrieval (ELVIRCOS)", "head_span":...
[ [ "two-pass algorithm", "approach" ], [ "recognition process", "passes" ], [ "large vocabulary speech recognition", "Extra Large (more than 1M words) Vocabulary COntinuous Speech recognition" ] ]
INTERSPEECH_2007_31_abs
[ [ "The", "paper", "assesses", "the", "capability", "of", "an", "HMM-based", "TTS", "system", "to", "produce", "German", "speech.", "The", "results", "are", "discussed", "in", "qualitative", "terms,", "and", "compared", "...
[ "The", "paper", "assesses", "the", "capability", "of", "an", "HMM-based", "TTS", "system", "to", "produce", "German", "speech.", "The", "results", "are", "discussed", "in", "qualitative", "terms,", "and", "compared", "over", "three", "different", "choices", "of"...
The paper assesses the capability of an HMM-based TTS system to produce German speech. The results are discussed in qualitative terms, and compared over three different choices of context features. In addition, the system is adapted to a small set of football announcements, in an exploratory attempt to synthe-sise expr...
[ { "end": 60, "id": "T1", "label": "Method", "start": 40, "text": "HMM-based TTS system" }, { "end": 85, "id": "T2", "label": "Material", "start": 72, "text": "German speech" }, { "end": 196, "id": "T3", "label": "OtherScientificTerm", "start": 180, ...
[ { "head": "HMM-based TTS system", "head_span": [ 40, 60 ], "relation": "USED-FOR", "tail": "German speech", "tail_span": [ 72, 85 ] }, { "head": "system", "head_span": [ 215, 221 ], "relation": "USED-FOR", "tail": "football anno...
[ [ "HMM-based TTS system", "system" ] ]
INTERSPEECH_2007_40_abs
[ [ "Information", "distillation", "aims", "to", "extract", "relevant", "pieces", "of", "information", "related", "to", "a", "given", "query", "from", "massive,", "possibly", "multilingual,", "audio", "and", "textual", "document",...
[ "Information", "distillation", "aims", "to", "extract", "relevant", "pieces", "of", "information", "related", "to", "a", "given", "query", "from", "massive,", "possibly", "multilingual,", "audio", "and", "textual", "document", "sources.", "In", "this", "paper", ",...
Information distillation aims to extract relevant pieces of information related to a given query from massive, possibly multilingual, audio and textual document sources. In this paper , we present our approach for using information extraction annotations to augment document retrieval for distillation. We take advantage...
[ { "end": 24, "id": "T1", "label": "Task", "start": 0, "text": "Information distillation" }, { "end": 168, "id": "T2", "label": "Material", "start": 102, "text": "massive, possibly multilingual, audio and textual document sources" }, { "end": 254, "id": "T3", ...
[ { "head": "information extraction annotations", "head_span": [ 220, 254 ], "relation": "USED-FOR", "tail": "document retrieval for distillation", "tail_span": [ 266, 301 ] } ]
[]
INTERSPEECH_2008_20_abs
[ [ "In", "the", "study", "of", "expressive", "speech", "communication,", "it", "is", "commonly", "accepted", "that", "the", "emotion", "perceived", "by", "the", "listener", "is", "a", "good", "approximation", "of", "the",...
[ "In", "the", "study", "of", "expressive", "speech", "communication,", "it", "is", "commonly", "accepted", "that", "the", "emotion", "perceived", "by", "the", "listener", "is", "a", "good", "approximation", "of", "the", "intended", "emotion", "conveyed", "by", ...
In the study of expressive speech communication, it is commonly accepted that the emotion perceived by the listener is a good approximation of the intended emotion conveyed by the speaker. This paper analyzes the validity of this assumption by comparing the mismatches between the assessments made by na¨ıve listeners an...
[ { "end": 47, "id": "T1", "label": "Task", "start": 16, "text": "expressive speech communication" }, { "end": 553, "id": "T2", "label": "Material", "start": 537, "text": "IEMOCAP database" }, { "end": 626, "id": "T3", "label": "Task", "start": 555, ...
[ { "head": "IEMOCAP database", "head_span": [ 537, 553 ], "relation": "USED-FOR", "tail": "discrete (categorical) and continuous (attribute) emotional assessments", "tail_span": [ 555, 626 ] } ]
[ [ "IEMOCAP database", "database" ] ]
INTERSPEECH_2008_21_abs
[ [ "This", "paper", "presents", "a", "research", "on", "the", "Czech", "talking", "head", "system.", "It", "gives", "an", "overview", "of", "methods", "used", "for", "visual", "speech", "animation,", "parameterization", "...
[ "This", "paper", "presents", "a", "research", "on", "the", "Czech", "talking", "head", "system.", "It", "gives", "an", "overview", "of", "methods", "used", "for", "visual", "speech", "animation,", "parameterization", "of", "a", "human", "face", "and", "a", "...
This paper presents a research on the Czech talking head system. It gives an overview of methods used for visual speech animation, parameterization of a human face and a tongue, necessary data sources and a synthesis method. A 3D animation model is used for a pseudo-muscular animation schema to create such animation of...
[ { "end": 63, "id": "T1", "label": "Task", "start": 38, "text": "Czech talking head system" }, { "end": 96, "id": "T2", "label": "Generic", "start": 89, "text": "methods" }, { "end": 129, "id": "T3", "label": "Task", "start": 106, "text": "visual sp...
[ { "head": "methods", "head_span": [ 89, 96 ], "relation": "USED-FOR", "tail": "visual speech animation", "tail_span": [ 106, 129 ] }, { "head": "3D animation model", "head_span": [ 227, 245 ], "relation": "USED-FOR", "tail": "ps...
[ [ "visual speech animation", "animation of visual speech" ], [ "forming articulatory trajectories", "It" ] ]
INTERSPEECH_2008_28_abs
[ [ "A", "critical", "step", "in", "encoding", "sound", "for", "neuronal", "processing", "occurs", "when", "the", "analog", "pressure", "wave", "is", "coded", "into", "discrete", "nerve-action", "potentials.", "Recent", "pool"...
[ "A", "critical", "step", "in", "encoding", "sound", "for", "neuronal", "processing", "occurs", "when", "the", "analog", "pressure", "wave", "is", "coded", "into", "discrete", "nerve-action", "potentials.", "Recent", "pool", "models", "of", "the", "inner", "hair"...
A critical step in encoding sound for neuronal processing occurs when the analog pressure wave is coded into discrete nerve-action potentials. Recent pool models of the inner hair cell synapse do not reproduce the dead time period after an intense stimulus, so we used visual inspection and automatic speech recognition ...
[ { "end": 33, "id": "T1", "label": "Task", "start": 19, "text": "encoding sound" }, { "end": 57, "id": "T2", "label": "Task", "start": 38, "text": "neuronal processing" }, { "end": 94, "id": "T3", "label": "OtherScientificTerm", "start": 74, "text":...
[ { "head": "pool models", "head_span": [ 150, 161 ], "relation": "USED-FOR", "tail": "inner hair cell synapse", "tail_span": [ 169, 192 ] }, { "head": "visual inspection", "head_span": [ 269, 286 ], "relation": "USED-FOR", "tail"...
[ [ "OA", "OA", "offset adaptation (OA) model" ], [ "Multi-layer perceptrons (MLPs)", "MLPs" ] ]
INTERSPEECH_2013_21_abs
[ [ "This", "work", "proposes", "a", "new", "research", "direction", "to", "address", "the", "lack", "of", "structures", "in", "traditional", "n-gram", "models.", "It", "is", "based", "on", "a", "weakly", "supervised", ...
[ "This", "work", "proposes", "a", "new", "research", "direction", "to", "address", "the", "lack", "of", "structures", "in", "traditional", "n-gram", "models.", "It", "is", "based", "on", "a", "weakly", "supervised", "dependency", "parser", "that", "can", "model...
This work proposes a new research direction to address the lack of structures in traditional n-gram models. It is based on a weakly supervised dependency parser that can model speech syntax without relying on any annotated training corpus. Labeled data is replaced by a few hand-crafted rules that encode basic syntactic...
[ { "end": 77, "id": "T1", "label": "OtherScientificTerm", "start": 59, "text": "lack of structures" }, { "end": 106, "id": "T2", "label": "Task", "start": 59, "text": "lack of structures in traditional n-gram models" }, { "end": 106, "id": "T3", "label": "M...
[ { "head": "weakly supervised dependency parser", "head_span": [ 125, 160 ], "relation": "USED-FOR", "tail": "speech syntax", "tail_span": [ 176, 189 ] }, { "head": "hand-crafted rules", "head_span": [ 274, 292 ], "relation": "USED-F...
[ [ "rules", "them" ], [ "discriminative model's posterior", "posterior" ] ]
INTERSPEECH_2013_28_abs
[ [ "We", "attack", "an", "inexplicably", "under-explored", "language", "genre", "of", "spoken", "language—lyrics", "in", "music—via", "completely", "unsuper-vised", "induction", "of", "an", "SMT-style", "stochastic", "transduction", ...
[ "We", "attack", "an", "inexplicably", "under-explored", "language", "genre", "of", "spoken", "language—lyrics", "in", "music—via", "completely", "unsuper-vised", "induction", "of", "an", "SMT-style", "stochastic", "transduction", "grammar", "for", "hip", "hop", "lyric...
We attack an inexplicably under-explored language genre of spoken language—lyrics in music—via completely unsuper-vised induction of an SMT-style stochastic transduction grammar for hip hop lyrics, yielding a fully-automatically learned challenge-response system that produces rhyming lyrics given an input. Unlike previ...
[ { "end": 74, "id": "T1", "label": "Material", "start": 26, "text": "under-explored language genre of spoken language" }, { "end": 90, "id": "T2", "label": "Material", "start": 75, "text": "lyrics in music" }, { "end": 129, "id": "T3", "label": "Method", ...
[ { "head": "fully-automatically learned challenge-response system", "head_span": [ 209, 262 ], "relation": "USED-FOR", "tail": "rhyming lyrics", "tail_span": [ 277, 291 ] }, { "head": "lyrics in music", "head_span": [ 75, 90 ], "rela...
[ [ "model", "fully-automatically learned challenge-response system", "approach" ] ]
INTERSPEECH_2013_31_abs
[ [ "In", "this", "paper,", "we", "present", "a", "digital", "signal", "processor", "(DSP)", "implementation", "of", "real-time", "statistical", "voice", "conversion", "(VC)", "for", "silent", "speech", "enhancement", "and", "...
[ "In", "this", "paper,", "we", "present", "a", "digital", "signal", "processor", "(DSP)", "implementation", "of", "real-time", "statistical", "voice", "conversion", "(VC)", "for", "silent", "speech", "enhancement", "and", "electrolaryngeal", "speech", "enhancement.", ...
In this paper, we present a digital signal processor (DSP) implementation of real-time statistical voice conversion (VC) for silent speech enhancement and electrolaryngeal speech enhancement. As a silent speech interface, we focus on non-audible murmur (NAM), which can be used in situations where audible speech is not ...
[ { "end": 73, "id": "T1", "label": "Method", "start": 28, "text": "digital signal processor (DSP) implementation" }, { "end": 120, "id": "T2", "label": "Method", "start": 77, "text": "real-time statistical voice conversion (VC)" }, { "end": 150, "id": "T3", ...
[ { "head": "digital signal processor (DSP) implementation", "head_span": [ 28, 73 ], "relation": "USED-FOR", "tail": "real-time statistical voice conversion (VC)", "tail_span": [ 77, 120 ] }, { "head": "real-time statistical voice conversion (VC)", "hea...
[ [ "NAM", "NAM", "non-audible murmur (NAM)" ], [ "real-time statistical voice conversion (VC)", "real-time VC", "real-time VC", "real-time VC" ], [ "DSP", "DSP" ], [ "VC", "it" ] ]
INTERSPEECH_2014_20_abs
[ [ "In", "this", "paper,", "we", "explore", "multilingual", "feature-level", "data", "sharing", "via", "Deep", "Neural", "Network", "(DNN)", "stacked", "bottleneck", "features.", "Given", "a", "set", "of", "available", "sourc...
[ "In", "this", "paper,", "we", "explore", "multilingual", "feature-level", "data", "sharing", "via", "Deep", "Neural", "Network", "(DNN)", "stacked", "bottleneck", "features.", "Given", "a", "set", "of", "available", "source", "languages,", "we", "apply", "language...
In this paper, we explore multilingual feature-level data sharing via Deep Neural Network (DNN) stacked bottleneck features. Given a set of available source languages, we apply language identification to pick the language most similar to the target language , for more efficient use of multilingual resources. Our experi...
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[ { "head": "Deep Neural Network (DNN) stacked bottleneck features", "head_span": [ 70, 123 ], "relation": "USED-FOR", "tail": "multilingual feature-level data sharing", "tail_span": [ 26, 65 ] }, { "head": "language identification", "head_span": [ ...
[]
INTERSPEECH_2014_28_abs
[ [ "This", "paper", "presents", "a", "novel", "statistical", "singing", "voice", "conversion", "(SVC)", "technique", "with", "direct", "waveform", "modification", "based", "on", "the", "spectrum", "differential", "that", "can", ...
[ "This", "paper", "presents", "a", "novel", "statistical", "singing", "voice", "conversion", "(SVC)", "technique", "with", "direct", "waveform", "modification", "based", "on", "the", "spectrum", "differential", "that", "can", "convert", "voice", "timbre", "of", "a"...
This paper presents a novel statistical singing voice conversion (SVC) technique with direct waveform modification based on the spectrum differential that can convert voice timbre of a source singer into that of a target singer without using a vocoder to generate converted singing voice waveforms. SVC makes it possible...
[ { "end": 80, "id": "T1", "label": "Method", "start": 28, "text": "statistical singing voice conversion (SVC) technique" }, { "end": 114, "id": "T2", "label": "Method", "start": 86, "text": "direct waveform modification" }, { "end": 149, "id": "T3", "label"...
[ { "head": "direct waveform modification", "head_span": [ 86, 114 ], "relation": "USED-FOR", "tail": "statistical singing voice conversion (SVC) technique", "tail_span": [ 28, 80 ] }, { "head": "spectrum differential", "head_span": [ 128, 14...
[ [ "statistical singing voice conversion (SVC) technique", "statistical conversion process", "method", "SVC" ] ]
INTERSPEECH_2014_31_abs
[ [ "During", "late-2013", "through", "early-2014", "NIST", "coordinated", "a", "special", "i-vector", "challenge", "based", "on", "data", "used", "in", "previous", "NIST", "Speaker", "Recognition", "Evaluations", "(SREs).", "Unli...
[ "During", "late-2013", "through", "early-2014", "NIST", "coordinated", "a", "special", "i-vector", "challenge", "based", "on", "data", "used", "in", "previous", "NIST", "Speaker", "Recognition", "Evaluations", "(SREs).", "Unlike", "evaluations", "in", "the", "SRE", ...
During late-2013 through early-2014 NIST coordinated a special i-vector challenge based on data used in previous NIST Speaker Recognition Evaluations (SREs). Unlike evaluations in the SRE series, the i-vector challenge was run entirely online and used fixed-length feature vectors projected into a low-dimensional space ...
[ { "end": 81, "id": "T1", "label": "Material", "start": 63, "text": "i-vector challenge" }, { "end": 156, "id": "T2", "label": "Material", "start": 113, "text": "NIST Speaker Recognition Evaluations (SREs)" }, { "end": 194, "id": "T3", "label": "Material", ...
[ { "head": "leading system", "head_span": [ 729, 743 ], "relation": "COMPARE", "tail": "baseline system", "tail_span": [ 800, 815 ] }, { "head": "SRE", "head_span": [ 509, 512 ], "relation": "COMPARE", "tail": "i-vector challenge...
[ [ "i-vector challenge", "i-vector challenge" ], [ "NIST Speaker Recognition Evaluations (SREs)", "SRE series", "SRE" ] ]
INTERSPEECH_2014_40_abs
[ [ "This", "paper", "deals", "with", "the", "problem", "of", "generating", "the", "fundamental", "frequency", "(F0)", "contour", "of", "speech", "from", "a", "text", "input", "for", "text-to-speech", "synthesis.", "We", "...
[ "This", "paper", "deals", "with", "the", "problem", "of", "generating", "the", "fundamental", "frequency", "(F0)", "contour", "of", "speech", "from", "a", "text", "input", "for", "text-to-speech", "synthesis.", "We", "have", "previously", "introduced", "a", "sta...
This paper deals with the problem of generating the fundamental frequency (F0) contour of speech from a text input for text-to-speech synthesis. We have previously introduced a statistical model describing the generating process of speech F0 contours, based on the discrete-time version of the Fujisaki model. One remark...
[ { "end": 96, "id": "T1", "label": "OtherScientificTerm", "start": 52, "text": "fundamental frequency (F0) contour of speech" }, { "end": 114, "id": "T2", "label": "Material", "start": 104, "text": "text input" }, { "end": 143, "id": "T3", "label": "Task", ...
[ { "head": "text input", "head_span": [ 104, 114 ], "relation": "USED-FOR", "tail": "fundamental frequency (F0) contour of speech", "tail_span": [ 52, 96 ] }, { "head": "fundamental frequency (F0) contour of speech", "head_span": [ 52, 96 ...
[ [ "fundamental frequency (F0) contour of speech", "speech F0 contours" ], [ "algorithm", "model", "model", "statistical model", "model", "it" ] ]
INTERSPEECH_2015_16_abs
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[ "Recently,", "Stacked", "Auto-Encoders", "(SAE)", "have", "been", "successfully", "used", "for", "learning", "imbalanced", "datasets.", "In", "this", "paper,", "for", "the", "first", "time,", "we", "propose", "to", "use", "a", "Neural", "Network", "classifier", ...
Recently, Stacked Auto-Encoders (SAE) have been successfully used for learning imbalanced datasets. In this paper, for the first time, we propose to use a Neural Network classifier furnished by an SAE structure for detecting the errors made by a strong Automatic Speech Recognition (ASR) system. Error detection on an au...
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[ { "head": "Stacked Auto-Encoders (SAE)", "head_span": [ 10, 37 ], "relation": "USED-FOR", "tail": "learning imbalanced datasets", "tail_span": [ 70, 98 ] }, { "head": "SAE structure", "head_span": [ 197, 210 ], "relation": "USED-FOR...
[ [ "Stacked Auto-Encoders (SAE)", "stacked auto-encoder architecture", "SAE", "SAE structure" ], [ "Automatic Speech Recognition (ASR) system", "ASR system" ], [ "measure", "mean absolute error" ] ]
INTERSPEECH_2015_9_abs
[ [ "We", "describe", "the", "use", "of", "text", "data", "scraped", "from", "the", "web", "to", "augment", "language", "models", "for", "Automatic", "Speech", "Recognition", "and", "Keyword", "Search", "for", "Low", "...
[ "We", "describe", "the", "use", "of", "text", "data", "scraped", "from", "the", "web", "to", "augment", "language", "models", "for", "Automatic", "Speech", "Recognition", "and", "Keyword", "Search", "for", "Low", "Resource", "Languages.", "We", "scrape", "text...
We describe the use of text data scraped from the web to augment language models for Automatic Speech Recognition and Keyword Search for Low Resource Languages. We scrape text from multiple genres including blogs, online news, translated TED talks, and subtitles. Using linearly interpolated language models, we find tha...
[ { "end": 32, "id": "T1", "label": "Material", "start": 23, "text": "text data" }, { "end": 53, "id": "T2", "label": "Material", "start": 50, "text": "web" }, { "end": 80, "id": "T3", "label": "Method", "start": 65, "text": "language models" }, ...
[ { "head": "language models", "head_span": [ 65, 80 ], "relation": "USED-FOR", "tail": "Automatic Speech Recognition", "tail_span": [ 85, 113 ] }, { "head": "Low Resource Languages", "head_span": [ 137, 159 ], "relation": "USED-FOR",...
[ [ "blogs", "blogs" ], [ "subtitles", "movie subtitles" ] ]
J05-1003
[ [ "This", "article", "considers", "approaches", "which", "rerank", "the", "output", "of", "an", "existing", "probabilistic", "parser", ".", "The", "base", "parser", "produces", "a", "set", "of", "candidate", "parses", "f...
[ "This", "article", "considers", "approaches", "which", "rerank", "the", "output", "of", "an", "existing", "probabilistic", "parser", ".", "The", "base", "parser", "produces", "a", "set", "of", "candidate", "parses", "for", "each", "input", "sentence", ",", "wi...
This article considers approaches which rerank the output of an existing probabilistic parser . The base parser produces a set of candidate parses for each input sentence , with associated probabilities that define an initial ranking of these parses . A second model then attempts to improve upon this i...
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[ { "head": "features", "head_span": [ 657, 665 ], "relation": "USED-FOR", "tail": "generative model", "tail_span": [ 620, 636 ] }, { "head": "boosting approach", "head_span": [ 749, 766 ], "relation": "USED-FOR", "tail": "ranking...
[ [ "probabilistic parser", "parser" ], [ "candidate parses", "parses" ], [ "approaches", "approach", "it" ], [ "features", "features" ], [ "boosting approach", "boosting method", "method", "model", "boosting approach", "boosting approach" ...
J05-4003
[ [ "We", "present", "a", "novel", "method", "for", "discovering", "parallel", "sentences", "in", "comparable,", "non-parallel", "corpora", ".", "We", "train", "a", "maximum", "entropy", "classifier", "that,", "given", "a", ...
[ "We", "present", "a", "novel", "method", "for", "discovering", "parallel", "sentences", "in", "comparable,", "non-parallel", "corpora", ".", "We", "train", "a", "maximum", "entropy", "classifier", "that,", "given", "a", "pair", "of", "sentences", ",", "can", "...
We present a novel method for discovering parallel sentences in comparable, non-parallel corpora . We train a maximum entropy classifier that, given a pair of sentences , can reliably determine whether or not they are translations of each other. Using this approach, we extract parallel data from large Ch...
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[ { "head": "comparable, non-parallel corpora", "head_span": [ 69, 101 ], "relation": "USED-FOR", "tail": "discovering parallel sentences", "tail_span": [ 33, 63 ] }, { "head": "parallel data", "head_span": [ 291, 304 ], "relation": "...
[ [ "method", "method", "maximum entropy classifier", "approach", "it" ], [ "MT system", "statistical machine translation system" ] ]
J81-1002
[ [ "This", "paper", "reports", "recent", "research", "into", "methods", "for", "creating", "natural", "language", "text", ".", "A", "new", "processing", "paradigm", "called", "Fragment-and-Compose", "has", "been", "created", ...
[ "This", "paper", "reports", "recent", "research", "into", "methods", "for", "creating", "natural", "language", "text", ".", "A", "new", "processing", "paradigm", "called", "Fragment-and-Compose", "has", "been", "created", "and", "an", "experimental", "system", "im...
This paper reports recent research into methods for creating natural language text . A new processing paradigm called Fragment-and-Compose has been created and an experimental system implemented in it. The knowledge to be expressed in text is first divided into small propositional units , which are then comp...
[ { "end": 48, "id": "T1", "label": "Generic", "start": 41, "text": "methods" }, { "end": 84, "id": "T2", "label": "Task", "start": 54, "text": "creating natural language text" }, { "end": 113, "id": "T3", "label": "Generic", "start": 94, "text": "pr...
[ { "head": "computational methods", "head_span": [ 821, 842 ], "relation": "USED-FOR", "tail": "KDS", "tail_span": [ 848, 851 ] }, { "head": "methods", "head_span": [ 41, 48 ], "relation": "USED-FOR", "tail": "creating natural la...
[ [ "methods", "processing paradigm", "Fragment-and-Compose", "paradigm", "Fragment-and-Compose paradigm" ], [ "KDS (Knowledge Delivery System)", "KDS" ] ]
J82-3002
[ [ "This", "paper", "gives", "an", "overall", "account", "of", "a", "prototype", "natural", "language", "question", "answering", "system", ",", "called", "Chat-80", ".", "Chat-80", "has", "been", "designed", "to", "be", ...
[ "This", "paper", "gives", "an", "overall", "account", "of", "a", "prototype", "natural", "language", "question", "answering", "system", ",", "called", "Chat-80", ".", "Chat-80", "has", "been", "designed", "to", "be", "both", "efficient", "and", "easily", "adap...
This paper gives an overall account of a prototype natural language question answering system , called Chat-80 . Chat-80 has been designed to be both efficient and easily adaptable to a variety of applications. The system is implemented entirely in Prolog , a programming language based on logic . With th...
[ { "end": 97, "id": "T1", "label": "Method", "start": 55, "text": "natural language question answering system" }, { "end": 116, "id": "T2", "label": "Method", "start": 109, "text": "Chat-80" }, { "end": 128, "id": "T3", "label": "Method", "start": 121, ...
[ { "head": "logic", "head_span": [ 304, 309 ], "relation": "USED-FOR", "tail": "programming language", "tail_span": [ 272, 292 ] }, { "head": "Chat-80", "head_span": [ 109, 116 ], "relation": "HYPONYM-OF", "tail": "natural langua...
[ [ "Chat-80", "Chat-80", "Chat-80", "system" ] ]
J86-1002
[ [ "A", "method", "for", "error", "correction", "of", "ill-formed", "input", "is", "described", "that", "acquires", "dialogue", "patterns", "in", "typical", "usage", "and", "uses", "these", "patterns", "to", "predict", "n...
[ "A", "method", "for", "error", "correction", "of", "ill-formed", "input", "is", "described", "that", "acquires", "dialogue", "patterns", "in", "typical", "usage", "and", "uses", "these", "patterns", "to", "predict", "new", "inputs.", "Error", "correction", "is",...
A method for error correction of ill-formed input is described that acquires dialogue patterns in typical usage and uses these patterns to predict new inputs. Error correction is done by strongly biasing parsing toward expected meanings unless clear evidence from the input shows the current sentence ...
[ { "end": 11, "id": "T1", "label": "Generic", "start": 5, "text": "method" }, { "end": 33, "id": "T2", "label": "Task", "start": 17, "text": "error correction" }, { "end": 55, "id": "T3", "label": "Material", "start": 39, "text": "ill-formed input" ...
[ { "head": "ill-formed input", "head_span": [ 39, 55 ], "relation": "USED-FOR", "tail": "error correction", "tail_span": [ 17, 33 ] }, { "head": "dialogue acquisition and tracking algorithm", "head_span": [ 340, 383 ], "relation": "U...
[ [ "dialogue patterns", "patterns" ], [ "error correction", "Error correction" ], [ "method", "error correction methodology" ] ]
J86-3001
[ [ "In", "this", "paper", "we", "explore", "a", "new", "theory", "of", "discourse", "structure", "that", "stresses", "the", "role", "of", "purpose", "and", "processing", "in", "discourse", ".", "In", "this", "theory,"...
[ "In", "this", "paper", "we", "explore", "a", "new", "theory", "of", "discourse", "structure", "that", "stresses", "the", "role", "of", "purpose", "and", "processing", "in", "discourse", ".", "In", "this", "theory,", "discourse", "structure", "is", "composed", ...
In this paper we explore a new theory of discourse structure that stresses the role of purpose and processing in discourse . In this theory, discourse structure is composed of three separate but interrelated components: the structure of the sequence of utterances (called the linguistic structure ), a s...
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[ { "head": "intentional structure", "head_span": [ 576, 597 ], "relation": "USED-FOR", "tail": "discourse-relevant purposes", "tail_span": [ 613, 640 ] }, { "head": "components", "head_span": [ 221, 231 ], "relation": "PART-OF", ...
[ [ "linguistic structure", "linguistic structure" ], [ "intentional structure", "intentional structure" ], [ "attentional state", "attentional state", "attentional state" ], [ "theory of attention, intention, and aggregation of utterances", "theory" ], [ "Disco...
J86-4002
[ [ "The", "goal", "of", "this", "work", "is", "the", "enrichment", "of", "human-machine", "interactions", "in", "a", "natural", "language", "environment", ".", "Because", "a", "speaker", "and", "listener", "cannot", "be",...
[ "The", "goal", "of", "this", "work", "is", "the", "enrichment", "of", "human-machine", "interactions", "in", "a", "natural", "language", "environment", ".", "Because", "a", "speaker", "and", "listener", "cannot", "be", "assured", "to", "have", "the", "same", ...
The goal of this work is the enrichment of human-machine interactions in a natural language environment . Because a speaker and listener cannot be assured to have the same beliefs , contexts , perceptions , backgrounds , or goals , at each point in a conversation , difficulties and mistakes arise...
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[ { "head": "natural language environment", "head_span": [ 81, 109 ], "relation": "FEATURE-OF", "tail": "human-machine interactions", "tail_span": [ 47, 73 ] }, { "head": "reference problems", "head_span": [ 828, 846 ], "relation": "H...
[ [ "miscommunication", "miscommunications", "them", "miscommunication" ] ]
J87-1003
[ [ "English", "is", "shown", "to", "be", "trans-context-free", "on", "the", "basis", "of", "coordinations", "of", "the", "respectively", "type", "that", "involve", "strictly", "syntactic", "cross-serial", "agreement", ".", "T...
[ "English", "is", "shown", "to", "be", "trans-context-free", "on", "the", "basis", "of", "coordinations", "of", "the", "respectively", "type", "that", "involve", "strictly", "syntactic", "cross-serial", "agreement", ".", "The", "agreement", "in", "question", "invol...
English is shown to be trans-context-free on the basis of coordinations of the respectively type that involve strictly syntactic cross-serial agreement . The agreement in question involves number in nouns and reflexive pronouns and is syntactic rather than semantic in nature because grammatical number i...
[ { "end": 9, "id": "T1", "label": "Material", "start": 2, "text": "English" }, { "end": 75, "id": "T2", "label": "OtherScientificTerm", "start": 62, "text": "coordinations" }, { "end": 157, "id": "T3", "label": "OtherScientificTerm", "start": 116, "...
[ { "head": "French", "head_span": [ 383, 389 ], "relation": "HYPONYM-OF", "tail": "languages", "tail_span": [ 363, 372 ] }, { "head": "nouns", "head_span": [ 210, 215 ], "relation": "CONJUNCTION", "tail": "reflexive pronouns", ...
[ [ "strictly syntactic cross-serial agreement", "agreement" ] ]
J87-3001
[ [ "This", "paper", "shows", "how", "dictionary", "word", "sense", "definitions", "can", "be", "analysed", "by", "applying", "a", "hierarchy", "of", "phrasal", "patterns", ".", "An", "experimental", "system", "embodying", ...
[ "This", "paper", "shows", "how", "dictionary", "word", "sense", "definitions", "can", "be", "analysed", "by", "applying", "a", "hierarchy", "of", "phrasal", "patterns", ".", "An", "experimental", "system", "embodying", "this", "mechanism", "has", "been", "implem...
This paper shows how dictionary word sense definitions can be analysed by applying a hierarchy of phrasal patterns . An experimental system embodying this mechanism has been implemented for processing definitions from the Longman Dictionary of Contemporary English . A property of this dictionary , exploited by ...
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[ { "head": "restricted vocabulary", "head_span": [ 351, 372 ], "relation": "USED-FOR", "tail": "word sense definitions", "tail_span": [ 382, 404 ] }, { "head": "robustness problems", "head_span": [ 1155, 1174 ], "relation": "FEATURE-...
[ [ "Longman Dictionary of Contemporary English", "dictionary", "it" ], [ "system", "system" ], [ "restricted vocabulary", "restricted vocabulary" ], [ "hierarchy of phrasal patterns", "mechanism" ] ]
J88-3002
[ [ "For", "intelligent", "interactive", "systems", "to", "communicate", "with", "humans", "in", "a", "natural", "manner,", "they", "must", "have", "knowledge", "about", "the", "system", "users", ".", "This", "paper", "exp...
[ "For", "intelligent", "interactive", "systems", "to", "communicate", "with", "humans", "in", "a", "natural", "manner,", "they", "must", "have", "knowledge", "about", "the", "system", "users", ".", "This", "paper", "explores", "the", "role", "of", "user", "mode...
For intelligent interactive systems to communicate with humans in a natural manner, they must have knowledge about the system users . This paper explores the role of user modeling in such systems . It begins with a characterization of what a user model is and how it can be used. The types of information ...
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[ { "head": "user modeling", "head_span": [ 176, 189 ], "relation": "PART-OF", "tail": "systems", "tail_span": [ 200, 207 ] }, { "head": "user modeling component", "head_span": [ 778, 801 ], "relation": "PART-OF", "tail": "system"...
[ [ "intelligent interactive systems", "they", "systems" ], [ "user model", "user modeling systems", "user model", "it", "user model", "User models", "they" ], [ "system", "system" ], [ "user modeling", "user modeling", "user modeling" ] ]
J89-4003
[ [ "A", "model", "is", "presented", "to", "characterize", "the", "class", "of", "languages", "obtained", "by", "adding", "reduplication", "to", "context-free", "languages", ".", "The", "model", "is", "a", "pushdown", "aut...
[ "A", "model", "is", "presented", "to", "characterize", "the", "class", "of", "languages", "obtained", "by", "adding", "reduplication", "to", "context-free", "languages", ".", "The", "model", "is", "a", "pushdown", "automaton", "augmented", "with", "the", "abilit...
A model is presented to characterize the class of languages obtained by adding reduplication to context-free languages . The model is a pushdown automaton augmented with the ability to check reduplication by using the stack in a new way. The class of languages generated is shown to lie strictly betwee...
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[ { "head": "model", "head_span": [ 4, 9 ], "relation": "USED-FOR", "tail": "class of languages", "tail_span": [ 45, 63 ] }, { "head": "stack", "head_span": [ 233, 238 ], "relation": "USED-FOR", "tail": "pushdown automaton", "...
[ [ "model", "model", "it", "model" ], [ "class of languages", "class of languages" ], [ "context-free languages", "context-free languages" ] ]
J90-3002
[ [ "This", "paper", "presents", "a", "specialized", "editor", "for", "a", "highly", "structured", "dictionary", ".", "The", "basic", "goal", "in", "building", "that", "editor", "was", "to", "provide", "an", "adequate", ...
[ "This", "paper", "presents", "a", "specialized", "editor", "for", "a", "highly", "structured", "dictionary", ".", "The", "basic", "goal", "in", "building", "that", "editor", "was", "to", "provide", "an", "adequate", "tool", "to", "help", "lexicologists", "prod...
This paper presents a specialized editor for a highly structured dictionary . The basic goal in building that editor was to provide an adequate tool to help lexicologists produce a valid and coherent dictionary on the basis of a linguistic theory . If we want valuable lexicons and grammars to achieve...
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[ { "head": "linguistic theory", "head_span": [ 243, 260 ], "relation": "USED-FOR", "tail": "dictionary", "tail_span": [ 212, 222 ] }, { "head": "structured dictionary", "head_span": [ 59, 81 ], "relation": "USED-FOR", "tail": "e...
[ [ "editor", "editor", "editor" ], [ "structured dictionary", "dictionary", "linguistic databases" ] ]
J97-1004
[ [ "\"To", "explain", "complex", "phenomena,", "an", "explanation", "system", "must", "be", "able", "to", "select", "information", "from", "a", "formal", "representation", "of", "domain", "knowledge", ",", "organize", "the", ...
[ "\"To", "explain", "complex", "phenomena,", "an", "explanation", "system", "must", "be", "able", "to", "select", "information", "from", "a", "formal", "representation", "of", "domain", "knowledge", ",", "organize", "the", "selected", "information", "into", "multis...
"To explain complex phenomena, an explanation system must be able to select information from a formal representation of domain knowledge , organize the selected information into multisentential discourse plans , and realize the discourse plans in text. Although recent years have witnessed significant progress in...
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[ { "head": "semantically rich, large-scale knowledge bases", "head_span": [ 529, 575 ], "relation": "USED-FOR", "tail": "explanation generation", "tail_span": [ 499, 521 ] }, { "head": "large-scale knowledge base", "head_span": [ 715, 741 ...
[ [ "explanation system", "explanation system", "robust explanation system" ], [ "evaluation methodology", "methodology", "evaluation" ] ]