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.
REF proposed to apply multiple-sequence alignment (MSA) for traditional, sentence-level PR.
Learning To Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment
Human Language Technology Conference And Meeting Of The North American Association For Computational Linguistics
Computer Science