Why and How to Pay Different Attention to Phrase Alignments of Different Intensities

This work studies comparatively two typical sentence pair classification tasks: textual entailment (TE) and answer selection (AS), observing that phrase alignments of different intensities contribute differently in these tasks. We address the problems of identifying phrase alignments of flexible granularity and pooling alignments of different intensities for these tasks. Examples for flexible granularity are alignments between two single words, between a single word and a phrase and between a short phrase and a long phrase. By intensity we roughly mean the degree of match, it ranges from identity over surface-form co-occurrence, rephrasing and other semantic relatedness to unrelated words as in lots of parenthesis text. Prior work (i) has limitations in phrase generation and representation, or (ii) conducts alignment at word and phrase levels by handcrafted features or (iii) utilizes a single attention mechanism over alignment intensities without considering the characteristics of specific tasks, which limits the system's effectiveness across tasks. We propose an architecture based on Gated Recurrent Unit that supports (i) representation learning of phrases of arbitrary granularity and (ii) task-specific focusing of phrase alignments between two sentences by attention pooling. Experimental results on TE and AS match our observation and are state-of-the-art.