  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.
