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        <title>A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching</title>
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    <name type="personal">
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    <abstract>We present a latent variable model for predicting the relationship between a pair of text sequences. Unlike previous auto-encoding–based approaches that consider each sequence separately, our proposed framework utilizes both sequences within a single model by generating a sequence that has a given relationship with a source sequence. We further extend the cross-sentence generating framework to facilitate semi-supervised training. We also define novel semantic constraints that lead the decoder network to generate semantically plausible and diverse sequences. We demonstrate the effectiveness of the proposed model from quantitative and qualitative experiments, while achieving state-of-the-art results on semi-supervised natural language inference and paraphrase identification.</abstract>
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%0 Conference Proceedings
%T A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching
%A Choi, Jihun
%A Kim, Taeuk
%A Lee, Sang-goo
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F choi-etal-2019-cross
%X We present a latent variable model for predicting the relationship between a pair of text sequences. Unlike previous auto-encoding–based approaches that consider each sequence separately, our proposed framework utilizes both sequences within a single model by generating a sequence that has a given relationship with a source sequence. We further extend the cross-sentence generating framework to facilitate semi-supervised training. We also define novel semantic constraints that lead the decoder network to generate semantically plausible and diverse sequences. We demonstrate the effectiveness of the proposed model from quantitative and qualitative experiments, while achieving state-of-the-art results on semi-supervised natural language inference and paraphrase identification.
%R 10.18653/v1/P19-1469
%U https://aclanthology.org/P19-1469
%U https://doi.org/10.18653/v1/P19-1469
%P 4747-4761

Markdown (Informal)

[A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching](https://aclanthology.org/P19-1469) (Choi et al., ACL 2019)

• A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching (Choi et al., ACL 2019)

ACL

• Jihun Choi, Taeuk Kim, and Sang-goo Lee. 2019. A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4747–4761, Florence, Italy. Association for Computational Linguistics.