1 research outputs found
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.Comment: 10 pages, 5 figure