  Attention mechanism has been proven effective on natural language processing.
This paper proposes an attention boosted natural language inference model named
aESIM by adding word attention and adaptive direction-oriented attention
mechanisms to the traditional Bi-LSTM layer of natural language inference
models, e.g. ESIM. This makes the inference model aESIM has the ability to
effectively learn the representation of words and model the local subsentential
inference between pairs of premise and hypothesis. The empirical studies on the
SNLI, MultiNLI and Quora benchmarks manifest that aESIM is superior to the
original ESIM model.
