Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment

Abstract

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.

Proceedings of HLT-NAACL 2003 (Human Language Technology Conference)

References in corpus (2)

• Finding consensus in speech recognition: word error minimization and other applications of confusion networks
• Bootstrapping Lexical Choice via Multiple-Sequence Alignment

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