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.

提出一种基于潜在变量的模型，可用于预测文本序列之间的关系，并在半监督自然语言推理和改写识别方面取得了最新进展。

一种跨句子潜变量模型用于半监督文本序列匹配

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

The past several years have seen remarkable progress in generative models
which produce convincing samples of images and other modalities. A shared
component of many powerful generative models is a decoder network, a parametric
deep neural net that defines a generative distribution. Examples include
variational autoencoders, generative adversarial networks, and generative
moment matching networks. Unfortunately, it can be difficult to quantify the
performance of these models because of the intractability of log-likelihood
estimation, and inspecting samples can be misleading. We propose to use
Annealed Importance Sampling for evaluating log-likelihoods for decoder-based
models and validate its accuracy using bidirectional Monte Carlo. The
evaluation code is provided at this https URL Using this
technique, we analyze the performance of decoder-based models, the
effectiveness of existing log-likelihood estimators, the degree of overfitting,
and the degree to which these models miss important modes of the data
distribution.

本文提出使用提议的退火重要性抽样方法对基于解码器的模型进行对数似然评估，并使用双向蒙特卡罗验证其精度，分析了解码器模型的性能，现有对数似然估计器的有效性，过拟合程度以及这些模型错过数据分布的重要模式情况。