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Language Modeling with Syntactic and Semantic Representation for Sentence Acceptability Predictions

Adam Ek
Centre for Linguistic Theory and Studies in Probability, Department of Philosophy, Linguistics and Theory of Science, University of Gothenburg, Sweden

Jean-Phillipe Bernardy
Centre for Linguistic Theory and Studies in Probability, Department of Philosophy, Linguistics and Theory of Science, University of Gothenburg, Sweden

Shalom Lappin
Centre for Linguistic Theory and Studies in Probability, Department of Philosophy, Linguistics and Theory of Science, University of Gothenburg, Sweden

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Ingår i: Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland

Linköping Electronic Conference Proceedings 167:8, s. 76--85

NEALT Proceedings Series 42:8, s. 76--85

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Publicerad: 2019-10-02

ISBN: 978-91-7929-995-8

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

In this paper, we investigate the effect of enhancing lexical embeddings in LSTM language models (LM) with syntactic and semantic representations. We evaluate the language models using perplexity, and we evaluate the performance of the models on the task of predicting human sentence acceptability judgments. We train LSTM language models on sentences automatically annotated with universal syntactic dependency roles (Nivre, 2016), dependency depth and universal semantic tags (Abzianidze et al., 2017) to predict sentence acceptability judgments. Our experiments indicate that syntactic tags lower perplexity, while semantic tags increase it. Our experiments also show that neither syntactic nor semantic tags improve the performance of LSTM language models on the task of predicting sentence acceptability judgments.

Nyckelord

Sentence acceptability Language modeling Semantic representations Syntactic representations

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