  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.
