Dependency Treelet Translation: Syntactically Informed Phrasal SMT

What it is about

The system takes a source sentence, parses it into a dependency tree, and projects that tree onto the target sentence through the word alignment. From the projected pairs it extracts treelet translation pairs: connected subtrees of the source with their aligned target words. A tree-based order model then decides how the target treelets are arranged, and a decoder combines the treelets with conventional phrase-table and language-model scores. Experiments use BLEU on a 10,000-sentence test set with one reference. The treelet system scores 40.66, against 38.83 for the phrasal decoder Pharaoh and 35.26 for an example-based hybrid. The gap is widest with small phrases and with small training sets: with single-word treelets the treelet system gets 37.50 BLEU where Pharaoh with the same settings gets 23.18. All differences are significant at the 99 percent level. The decoder is slower than Pharaoh, about 10 sentences per minute against 162.

Ideas

• I1. Treelet pairs as units of the encoder. A treelet is a connected subtree of the source with its target words, which is the chunk that open problem 3 asks for. Extract treelet pairs from verse pairs, using the projected head of each target word, and keep the pairs whose count passes a threshold.
• I2. A count-based order model. For each head and child relation, count whether the child precedes its head in the aligned target order. The result is a small table that can serve the linearizer side tables in en/src/lin.rs, and it is the count form of the Hebrew order normaliser (open problem 7).
• I3. Phrase-size sweep as the paper does in its Table 5.2. Run the meta-world encoder with chunks of one word, two words and four words on the same dev verses, and report the residual for each size.
• I4. Training-size sweep as in Table 5.3. The tree model gains most at small corpora, so repeat the sweep on subsets of the Bible pairs to check whether the gain holds for the small translators.

For Mova

The code is tools/translate (the encoder in profile.rs, the lexicon in meta.rs) and the order table for en/src/lin.rs. The model here is counted, not neural, so it fits the ordered-map design. The first measure is the phrase-size sweep of I3, since it shows whether one-word chunks with a tree order model are enough.

Topics: Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.3115/1219840.1219874

@inproceedings{quirk2005dependency,
  title = {{Dependency Treelet Translation: Syntactically Informed Phrasal SMT}},
  author = {Quirk, Chris and Menezes, Arul and Cherry, Colin},
  booktitle = {Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05)},
  year = {2005},
  doi = {10.3115/1219840.1219874},
  url = {https://doi.org/10.3115/1219840.1219874}
}

References

20 references, 3 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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