In sentence classification tasks, additional contexts, such as the neighboring sentences, may improve the accuracy of the classifier. However, such contexts are domain-dependent and thus cannot be used for another classification task with an inappropriate domain. In contrast, we propose the use of translated sentences as context that is always available regardless of the domain. We find that naive feature expansion of translations gains only marginal improvements and may decrease the performance of the classifier, due to possible inaccurate translations thus producing noisy sentence vectors. To this end, we present multiple context fixing attachment (MCFA), a series of modules attached to multiple sentence vectors to fix the noise in the vectors using the other sentence vectors as context. We show that our method performs competitively compared to previous models, achieving best classification performance on multiple data sets. We are the first to use translations as domain-free contexts for sentence classification.

本文提出了使用翻译句子作为上下文信息，以解决在句子分类任务中常常面临上下文信息与任务领域不匹配的问题。作者提出的多重上下文固定模块在多个句向量的基础上修复了向量中的噪音，实现了与之前模型相比更好的分类性能。此外，本文还在该领域首次使用翻译作为无领域依赖的上下文信息。

使用翻译作为句子分类的额外上下文