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.
They don't report how to generate inferences with the retrieved rules, neither. REF solves the text-to-text generation problem of sentence-level paraphrasing by applying multiplesequence alignment to sentences gathered from unannotated comparable corpora (articles in different newspapers but about the same event): it learns a set of paraphrasing patterns represented by word lattice pairs and automatically determines how to apply these patterns to rewrite new sentences.
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