Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport
Selecting input features of top relevance has become a popular method for building self-explaining models. In this work, we extend this selective rationalization approach to text matching, where the goal is to jointly select and align text pieces, such as tokens or sentences, as a justification for the downstream prediction... (read more)
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Tasks
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SNLI
FEVER
MultiRC
e-SNLI
Multi-News
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