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1
How Well Do LSTM Language Models Learn Filler-gap Dependencies?
In: Proceedings of the Society for Computation in Linguistics (2022)
Abstract: This paper revisits the question of what LSTMs know about the syntax of filler-gap dependencies in English. One contribution of this paper is to adjust the metrics used by Wilcox et al. 2018 and show that their language models (LMs) learn embedded wh-questions -- a kind of filler-gap dependencies -- better than they originally claimed. Another contribution of this paper is to examine four additional filler-gap dependency constructions to see whether LMs perform equally on all types of filler-gap dependencies. We find that different constructions are learned to different extents, and there is a correlation between performance and frequency of constructions in the Penn Treebank Wall Street Journal corpus.
Keyword: Computational Linguistics; LSTM; psycholinguistics; Psycholinguistics and Neurolinguistics; RNN; syntax
URL: https://scholarworks.umass.edu/scil/vol5/iss1/7
https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1256&context=scil
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2
Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation ...
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3
A Resource for Computational Experiments on Mapudungun ...
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4
Language Technologies for Humanitarian Aid ...
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5
Language Technologies for Humanitarian Aid ...
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6
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations ...
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7
ParaMor: Finding Paradigms across Morphology ...
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8
ParaMor: Minimally-Supervised Induction of Paradigm Structure and Morphological Analysis ...
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ParaMor: Finding Paradigms across Morphology ...
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10
Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning ...
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11
Proceedings of the Grammar Engineering Across Frameworks (GEAF) 2015 Workshop
Bender, Emily,; Levin, Lori; Müller, Stefan. - : HAL CCSD, 2015. : Association for Computational Linguistics, 2015
In: The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-01181344 ; The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Jul 2015, Beijing, China. Association for Computational Linguistics, 2015 (2015)
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12
Statistical modality tagging from rule-based annotations and crowdsourcing ...
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13
Use of Modality and Negation in Semantically-Informed Syntactic MT ...
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14
A Modality Lexicon and its use in Automatic Tagging ...
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15
Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ...
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16
Use of Modality and Negation in Semantically-Informed Syntactic MT
In: DTIC (2012)
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17
Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing
Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : Association for Computational Linguistics, 2012
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18
Use of Modality and Negation in Semantically-Informed Syntactic MT
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19
Interlingual annotation of parallel text corpora: a new framework for annotation and evaluation
In: Natural language engineering. - Cambridge : Cambridge University Press 16 (2010) 3, 197-243
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20
Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ...
Baker, Kathryn; Bloodgood, Michael; Callison-Burch, Chris. - : Digital Repository at the University of Maryland, 2010
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