Paper: Learning To Recognize Features Of Valid Textual Entailments

ACL ID N06-1006
Title Learning To Recognize Features Of Valid Textual Entailments
Venue Human Language Technologies
Session Main Conference
Year 2006

This paper advocates a new architecture for tex- tual inference in which finding a good alignment is separated from evaluating entailment. Current ap- proaches to semantic inference in question answer- ing and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a lo- cally decomposable matching score. We argue that there are significant weaknesses in this approach, including flawed assumptions of monotonicity and locality. Instead we propose a pipelined approach where alignment is followed by a classification step, in which we extract features representing high-level characteristics of the entailment prob- lem, and pass the resulting feature vector to a statis- tical classifier trained on development data....