Syntax-based Alignment of Multiple Translations: Extracting Paraphrases and Generating New Sentences
What it is about
The authors take groups of 11 English translations of the same Chinese sentence (899 groups) and parse each one. The parse trees are merged top-down: two nodes join when they expand by the same rule, and a keyword check stops a join when the content words under the two nodes overlap elsewhere. The merged forest becomes a word lattice whose paths are paraphrases; a squeezing step then joins the far ends of equal-word edges that share a node. Four judges rated 600 paraphrase pairs: 77–85% of the syntax-based pairs were correct, against 63–68% for a co-training baseline. Multiple-sequence alignment produced lattices with many bad paths, where a word repeats though no source sentence repeats it (repetition ratio 0.030 against 0.0035 for this method).
Ideas
- I1. Join only where the parents agree. When
tools/translatemerges two meta tokens from two translations of a verse into one concept (meta.rs), allow the join only if the heads they hang from already joined, and go top-down, stopping at the first disagreement. This is the paper's rule against open problems 1 and 2. Measure the number of joins and the reader plus encoder bits, run twice. - I2. A keyword veto. Before a join, check whether the content words under each side occur elsewhere in the same verse; if they do, do not join. Count how many of today's merges (for example be with have) it removes.
- I3. A squeeze step on the meta-world. When two edges with the same concept leave one node, join their far ends. Measure how often a concept appears twice in one reading of a verse where the source has it once, before and after.
- I4. A held-out gain. Hold one translation of a verse out, and compare its edit distance to the meta-world paths with its edit distance to the other translations as a plain set (the paper's ed gain, mean 0.91 over 899 groups). It is a measure of the lexicon that uses no test set.
For Mova
The paper is the nearest published method to the meta-world: several translations of one text merged into one structure, with rules and no learned model. Its target is paraphrase, not translation, so only the reader and the lexicon carry over. Measure the join counts under I1 and I2 on dev before any claim about the residual.
Cite
@inproceedings{pang2003syntax,
title = {{Syntax-based Alignment of Multiple Translations: Extracting Paraphrases and Generating New Sentences}},
author = {Pang, Bo and Knight, Kevin and Marcu, Daniel},
booktitle = {Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics},
year = {2003},
url = {https://aclanthology.org/N03-1024/}
}References
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