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        <title>Deep Generative Model for Joint Alignment and Word Representation</title>
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    <abstract>This work exploits translation data as a source of semantically relevant learning signal for models of word representation. In particular, we exploit equivalence through translation as a form of distributional context and jointly learn how to embed and align with a deep generative model. Our EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments. Besides, it embeds words as posterior probability densities, rather than point estimates, which allows us to compare words in context using a measure of overlap between distributions (e.g. KL divergence). We investigate our model’s performance on a range of lexical semantics tasks achieving competitive results on several standard benchmarks including natural language inference, paraphrasing, and text similarity.</abstract>
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%0 Conference Proceedings
%T Deep Generative Model for Joint Alignment and Word Representation
%A Rios, Miguel
%A Aziz, Wilker
%A Sima’an, Khalil
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F rios-etal-2018-deep
%X This work exploits translation data as a source of semantically relevant learning signal for models of word representation. In particular, we exploit equivalence through translation as a form of distributional context and jointly learn how to embed and align with a deep generative model. Our EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments. Besides, it embeds words as posterior probability densities, rather than point estimates, which allows us to compare words in context using a measure of overlap between distributions (e.g. KL divergence). We investigate our model’s performance on a range of lexical semantics tasks achieving competitive results on several standard benchmarks including natural language inference, paraphrasing, and text similarity.
%R 10.18653/v1/N18-1092
%U https://aclanthology.org/N18-1092
%U https://doi.org/10.18653/v1/N18-1092
%P 1011-1023

Markdown (Informal)

[Deep Generative Model for Joint Alignment and Word Representation](https://aclanthology.org/N18-1092) (Rios et al., NAACL 2018)

• Deep Generative Model for Joint Alignment and Word Representation (Rios et al., NAACL 2018)

ACL

• Miguel Rios, Wilker Aziz, and Khalil Sima’an. 2018. Deep Generative Model for Joint Alignment and Word Representation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1011–1023, New Orleans, Louisiana. Association for Computational Linguistics.