  Recognizing textual entailment is a fundamental task in a variety of text
mining or natural language processing applications. This paper proposes a
simple neural model for RTE problem. It first matches each word in the
hypothesis with its most-similar word in the premise, producing an augmented
representation of the hypothesis conditioned on the premise as a sequence of
word pairs. The LSTM model is then used to model this augmented sequence, and
the final output from the LSTM is fed into a softmax layer to make the
prediction. Besides the base model, in order to enhance its performance, we
also proposed three techniques: the integration of multiple word-embedding
library, bi-way integration, and ensemble based on model averaging.
Experimental results on the SNLI dataset have shown that the three techniques
are effective in boosting the predicative accuracy and that our method
outperforms several state-of-the-state ones.
