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Title:	Putting words in context: LSTM language models and lexical ambiguity
Author:	Boleda, Gemma; Gulordava, Kristina; Aina, Laura
Abstract:	Comunicació presentada al 57th Annual Meeting of the Association for Computational Linguistic (ACL 2019), celebrat els dies 28 de juliol a 2 d'agost de 2019 a Florència, Itàlia.
Abstract:	In neural network models of language, words are commonly represented using context invariant representations (word embeddings) which are then put in context in the hidden layers. Since words are often ambiguous, representing the contextually relevant information is not trivial. We investigate how an LSTM language model deals with lexical ambiguity in English, designing a method to probe its hidden representations for lexical and contextual information about words. We find that both types of information are represented to a large extent, but also that there is room for improvement for contextual information.
Abstract:	This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 715154), and from the Ramón y Cajal programme (grant RYC-2015-18907).
Subject(s):	-Language models -Lexical ambiguity -Neural networks
Rights:	© ACL, Creative Commons Attribution 4.0 License http://creativecommons.org/licenses/by/4.0/
Document type:	Conference Object Article - Published version
Published by:	ACL (Association for Computational Linguistics)
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