The RepEval 2017 Shared Task aims to evaluate natural language understanding
models for sentence representation, in which a sentence is represented as a
fixed-length vector with neural networks and the quality of the representation
is tested with a natural language inference task. This paper describes our
system (alpha) that is ranked among the top in the Shared Task, on both the
in-domain test set (obtaining a 74.9% accuracy) and on the cross-domain test
set (also attaining a 74.9% accuracy), demonstrating that the model generalizes
well to the cross-domain data...
Our model is equipped with intra-sentence
gated-attention composition which helps achieve a better performance. In
addition to submitting our model to the Shared Task, we have also tested it on
the Stanford Natural Language Inference (SNLI) dataset. We obtain an accuracy
of 85.5%, which is the best reported result on SNLI when cross-sentence
attention is not allowed, the same condition enforced in RepEval 2017.