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        <title>A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations</title>
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    <abstract>We propose a generative model for a sentence that uses two latent variables, with one intended to represent the syntax of the sentence and the other to represent its semantics. We show we can achieve better disentanglement between semantic and syntactic representations by training with multiple losses, including losses that exploit aligned paraphrastic sentences and word-order information. We evaluate our models on standard semantic similarity tasks and novel syntactic similarity tasks. Empirically, we find that the model with the best performing syntactic and semantic representations also gives rise to the most disentangled representations.</abstract>
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%0 Conference Proceedings
%T A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations
%A Chen, Mingda
%A Tang, Qingming
%A Wiseman, Sam
%A Gimpel, Kevin
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F chen-etal-2019-multi
%X We propose a generative model for a sentence that uses two latent variables, with one intended to represent the syntax of the sentence and the other to represent its semantics. We show we can achieve better disentanglement between semantic and syntactic representations by training with multiple losses, including losses that exploit aligned paraphrastic sentences and word-order information. We evaluate our models on standard semantic similarity tasks and novel syntactic similarity tasks. Empirically, we find that the model with the best performing syntactic and semantic representations also gives rise to the most disentangled representations.
%R 10.18653/v1/N19-1254
%U https://aclanthology.org/N19-1254
%U https://doi.org/10.18653/v1/N19-1254
%P 2453-2464

Markdown (Informal)

[A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations](https://aclanthology.org/N19-1254) (Chen et al., NAACL 2019)

• A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations (Chen et al., NAACL 2019)

ACL

• Mingda Chen, Qingming Tang, Sam Wiseman, and Kevin Gimpel. 2019. A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2453–2464, Minneapolis, Minnesota. Association for Computational Linguistics.