Paraphrasing with Bilingual Parallel Corpora

What it is about

The paper shows that paraphrases can be extracted from a bilingual parallel corpus, which is far more common than monolingual parallel text. Two English phrases that align to the same foreign phrase are taken as paraphrases. The paraphrase probability is the sum over foreign phrases of the probability of the English paraphrase given the foreign phrase, times the probability of that foreign phrase given the first English phrase, both taken from phrase alignments of statistical MT. The paper also controls the word sense by requiring the same foreign phrase in a test sentence, and adds language-model re-ranking. The evaluation substitutes the top paraphrase into 289 sentences and judges correctness. With manual alignments, 74.9% of the substituted sentences are judged correct and 84.7% keep the meaning. With automatic alignments the accuracy is 48.9%, rising to 57% with word-sense control and 61.9% with language-model re-ranking; several European corpora together reach 55%. The gap between manual and automatic alignment is the main source of error.

Ideas

I1. A count form of the translator's lexicon: the source chunk (Hebrew) plays the role of the foreign phrase, the English chunks aligned to it are the candidate paraphrases, and the probability is a product of two counts. No neural model is involved, and each merge carries its counts.
I2. Context control for merges: accept a merge in a verse only if both words were aligned to the same source chunk in that verse. This is the paper's word-sense trick applied to the reader; measure how many merges survive it.
I3. Report accuracy split by the source of the alignment, as the paper does. For Mova the Strong's-number alignment (open problem 7) is the gold side and IBM Model 1 is the automatic side; the size of the gap says how much the alignment work is worth.

For Mova

This paper is the origin of the pivot idea that the translator's lexicon (open problem 2) needs, and its main lesson is that alignment noise, not the pivot, limits accuracy. The tools are tools/translate/ibm.rs and hebrew.rs. Summary only, as the licence is non-commercial.

Topics: Word sense disambiguation · Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.3115/1219840.1219914

@inproceedings{bannard2005paraphrasing,
  title = {{Paraphrasing with Bilingual Parallel Corpora}},
  author = {Bannard, Colin and Callison-Burch, Chris},
  booktitle = {Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05)},
  year = {2005},
  doi = {10.3115/1219840.1219914},
  url = {https://doi.org/10.3115/1219840.1219914}
}

References

18 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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Mona Diab and Philip Resnik. 2002. An unsupervised method for word sense tagging using parallel corpora. In Proceedings of ACL. find
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