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Hits 1 – 3 of 3
1
Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks
McCoy, R. Thomas
;
Frank, Robert
;
Linzen, Tal
In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 125-140 (2020) (2020)
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2
Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks ...
McCoy, R. Thomas
;
Frank, Robert
;
Linzen, Tal
. - : arXiv, 2018
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3
TAG Parsing with Neural Networks and Vector Representations of Supertags
Kasai, Jungo
;
Frank, Robert
;
Mccoy, R. Thomas
;
Rambow, Owen
;
Nasr, Alexis
In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, ; Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-01771494 ; Conference on Empirical Methods in Natural Language Processing, Sep 2017, Copenhague, Denmark. pp.1712 - 1722 (2017)
Abstract:
International audience ; We present supertagging-based models for Tree Adjoining Grammar parsing that use neural network architectures and dense vector representation of supertags (elementary trees) to achieve state-of-the-art performance in unlabeled and labeled attachment scores. The shift-reduce parsing model eschews lexical information entirely , and uses only the 1-best supertags to parse a sentence, providing further support for the claim that supertagging is " almost parsing. " We demonstrate that the embedding vector representations the parser induces for supertags possess linguistically interpretable structure, supporting analogies between grammatical structures like those familiar from recent work in distri-butional semantics. This dense representation of supertags overcomes the drawbacks for statistical models of TAG as compared to CCG parsing, raising the possibility that TAG is a viable alternative for NLP tasks that require the assignment of richer structural descriptions to sentences.
Keyword:
[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
URL:
https://hal.archives-ouvertes.fr/hal-01771494/file/D17-1180.pdf
https://hal.archives-ouvertes.fr/hal-01771494/document
https://hal.archives-ouvertes.fr/hal-01771494
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