A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching

Information

Title A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching
Authors Jihun Choi, Taeuk Kim, Sang-goo Lee
Year 2019 / 7
Keywords natural language inference, paraphrase identificatoin, semi-supervised learning, latent variable model
Publication Type International Conference
Publication The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)
Link url

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.