Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment
Regina Barzilay and Lillian Lee
Proceedings of HLT-NAACL, pp. 16--23, 2003

We address the text-to-text generation problem of sentence-level paraphrasing --- a phenomenon distinct from and more difficult than word- or phrase-level paraphrasing. Our approach applies multiple-sequence alignment to sentences gathered from unannotated comparable corpora: it learns a set of paraphrasing patterns represented by word lattice pairs and automatically determines how to apply these patterns to rewrite new sentences. The results of our evaluation experiments show that the system derives accurate paraphrases, outperforming baseline systems.

Press mentions

@inproceedings{Barzilay+Lee:03a, author = {Regina Barzilay and Lillian Lee}, title = {Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment}, year = {2003}, pages = {16--23}, booktitle = {Proceedings of HLT-NAACL} }

This paper is based upon work supported in part by the National Science Foundation under ITR/IM grant IIS-0081334 and a Sloan Research Fellowship. Any opinions, findings, and conclusions or recommendations expressed above are those of the authors and do not necessarily reflect the views of the National Science Foundation or the Sloan Foundation.