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Hits 101 – 120 of 20.397

101
How Well Do LSTM Language Models Learn Filler-gap Dependencies?
In: Proceedings of the Society for Computation in Linguistics (2022)
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102
A split-gesture, competitive, coupled oscillator model of syllable structure predicts the emergence of edge gemination and degemination
In: Proceedings of the Society for Computation in Linguistics (2022)
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103
Linguistic Complexity and Planning Effects on Word Duration in Hindi Read Aloud Speech
In: Proceedings of the Society for Computation in Linguistics (2022)
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104
Learning Argument Structures with Recurrent Neural Network Grammars
In: Proceedings of the Society for Computation in Linguistics (2022)
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105
MaxEnt Learners are Biased Against Giving Probability to Harmonically Bounded Candidates
In: Proceedings of the Society for Computation in Linguistics (2022)
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106
Evaluating Structural Economy Claims in Relative Clause Attachment
In: Proceedings of the Society for Computation in Linguistics (2022)
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107
A Model Theoretic Perspective on Phonological Feature Systems
In: Proceedings of the Society for Computation in Linguistics (2022)
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108
Representing Multiple Dependencies in Prosodic Structures
In: Proceedings of the Society for Computation in Linguistics (2022)
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109
Incremental Acquisition of a Minimalist Grammar using an SMT-Solver
In: Proceedings of the Society for Computation in Linguistics (2022)
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110
Concurrent hidden structure & grammar learning
In: Proceedings of the Society for Computation in Linguistics (2022)
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111
Can language models capture syntactic associations without surface cues? A case study of reflexive anaphor licensing in English control constructions
In: Proceedings of the Society for Computation in Linguistics (2022)
Abstract: We examine GPT-2 (Radford et al., 2019), which is trained only on surface strings, to see whether or not the model makes correct predictions about the agreement patterns of a reflexive anaphor in English control constructions. Our findings show that GPT-2 struggles with transitive subject control constructions, but does well on transitive object control constructions. One reason might be that the model tries to associate the anaphor with the closest noun phrase. Moreover, while we find that a model with a larger number of parameters shows higher accuracy on the tasks related to subject control constructions, performance remains below chance.
Keyword: Computational Linguistics; control constructions; GPT-2; long-distance dependencies; reflexive anaphor; Syntax
URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1241&context=scil
https://scholarworks.umass.edu/scil/vol5/iss1/19
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112
Universal Dependencies and Semantics for English and Hebrew Child-directed Speech
In: Proceedings of the Society for Computation in Linguistics (2022)
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113
Learning Input Strictly Local Functions: Comparing Approaches with Catalan Adjectives
In: Proceedings of the Society for Computation in Linguistics (2022)
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114
Learning Constraints on Wh-Dependencies by Learning How to Efficiently Represent Wh-Dependencies: A Developmental Modeling Investigation With Fragment Grammars
In: Proceedings of the Society for Computation in Linguistics (2022)
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115
Typological Implications of Tier-Based Strictly Local Movement
In: Proceedings of the Society for Computation in Linguistics (2022)
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116
Parsing Early Modern English for Linguistic Search
In: Proceedings of the Society for Computation in Linguistics (2022)
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117
When Classifying Arguments, BERT Doesn't Care About Word Order. Except When It Matters
In: Proceedings of the Society for Computation in Linguistics (2022)
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118
Remodelling complement coercion interpretation
In: Proceedings of the Society for Computation in Linguistics (2022)
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119
Inferring Inferences: Relational Propositions for Argument Mining
In: Proceedings of the Society for Computation in Linguistics (2022)
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120
ANLIzing the Adversarial Natural Language Inference Dataset
In: Proceedings of the Society for Computation in Linguistics (2022)
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