Refining Raw Sentence Representations for Textual Entailment Recognition via Attention
Jorge Balazs, Edison Marrese-Taylor, Pablo Loyola, Yutaka Matsuo · 2017
In this paper we present the model used by the team Rivercorners for the 2017 RepEval shared task.First, our model separately encodes a pair of sentences into variable-length representations by using a bidirectional LSTM.Later, it creates fixed-length raw representations by means of simple aggregation functions, which are then refined using an attention mechanism.Finally it combines the refined representations of both sentences into a single vector to be used for classification.With this model we obtained test accuracies of 72.057% and 72.055% in the matched and mismatched evaluation tracks respectively, outperforming the LSTM baseline, and obtaining performances similar to a model that relies on shared information between sentences (ESIM).When using an ensemble both accuracies increased to 72.247% and 72.827% respectively.