This paper advocates a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally 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 problem, and pass the resulting feature vector to a statis- be seen in this light.) In this paper, we highlight the tical classifier trained on development data. We report results on data from the 2005 Pascal RTE Challenge which surpass previously reported results for alignment-based systems.
%0 Conference Paper
%1 maccartney06learning
%A MacCartney, Bill
%A Grenager, Trond
%A de Marneffe, Marie-Catherine
%A Cer, Daniel
%A Manning, Christopher D.
%B Proceedings of the North American Association of Computational Linguistics
%D 2006
%K 2006 stanford parsetree parser NT2OD nlp
%T Learning to recognize features of valid textual entailments
%U http://nlp.stanford.edu/pubs/rte-naacl06.pdf
%X This paper advocates a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally 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 problem, and pass the resulting feature vector to a statis- be seen in this light.) In this paper, we highlight the tical classifier trained on development data. We report results on data from the 2005 Pascal RTE Challenge which surpass previously reported results for alignment-based systems.
@inproceedings{maccartney06learning,
abstract = {This paper advocates a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally 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 problem, and pass the resulting feature vector to a statis- be seen in this light.) In this paper, we highlight the tical classifier trained on development data. We report results on data from the 2005 Pascal RTE Challenge which surpass previously reported results for alignment-based systems.},
added-at = {2007-02-25T19:57:21.000+0100},
author = {MacCartney, Bill and Grenager, Trond and de Marneffe, Marie-Catherine and Cer, Daniel and Manning, Christopher D.},
biburl = {https://www.bibsonomy.org/bibtex/28cad58a130c30c8e7bf32f28b2d72a7b/butonic},
booktitle = {Proceedings of the North American Association of Computational Linguistics},
interhash = {a5bf4791f3706089fef1b7b16a691144},
intrahash = {8cad58a130c30c8e7bf32f28b2d72a7b},
keywords = {2006 stanford parsetree parser NT2OD nlp},
organization = {The Stanford Natural Language Processing Group},
school = {Stanford University},
timestamp = {2007-02-25T19:57:21.000+0100},
title = {Learning to recognize features of valid textual entailments},
url = {http://nlp.stanford.edu/pubs/rte-naacl06.pdf},
year = 2006
}