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

What it is about

The paper addresses sentence-level paraphrase, which is harder than word or phrase paraphrase, using comparable news corpora: different sources reporting the same events. Sentences are first clustered by topic and similarity within each source. Each cluster is aligned by multiple-sequence alignment into a word lattice, whose shared backbone nodes are the fixed words and whose gaps are slots. Pairs of lattices from different sources form paraphrase patterns, and a new sentence that matches one lattice is rewritten with the other. On held-out articles about violence in the Middle East, the system paraphrases 59 of 484 sentences (12.2%), against 7 sentences that appeared in the training set, so the lattices generalise far better than exact matching. Human judges rated the generated pairs, and the output was compared with a WordNet synonym baseline. Coverage is high on short articles and low on long ones, which the authors trace to the lattice matching.

Ideas

• I1. Align all English renderings of one verse across the translations in the same way. The shared backbone is the frame-preserving part of the verse, and the slots are the author delta. A slot filled in three or more translations goes to the standard; a slot filled in one goes to the author.
• I2. Use lattice pairs as encoder rules: when the meta tokens of a verse match the backbone of one lattice, the encoder writes the slot fillers of the other translation. The unit is a chunk with its inner relations, not a single word (open problem 3).
• I3. Report coverage (the share of sentences the rules can rewrite) next to the error rate, as the paper does. For the translator, the coverage of author deltas on held-out verses is the number to track alongside the residual.

For Mova

The paper addresses the author delta (open problem 6) and the encoder's chunks (open problem 3). The data are news, not verses, so the lattice matching must be tried on the verse pairs before any gain is claimed. Summary only, as the licence is non-commercial.

Topics: Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.3115/1073445.1073448

@inproceedings{barzilay2003learning,
  title = {{Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment}},
  author = {Barzilay, Regina and Lee, Lillian},
  booktitle = {Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics},
  year = {2003},
  doi = {10.3115/1073445.1073448},
  url = {https://doi.org/10.3115/1073445.1073448}
}

References

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