A New String-to-Dependency Machine Translation Algorithm with a Target Dependency Language Model

What it is about

The paper proposes a string-to-dependency statistical MT system. The source side of each rule is a string, as in the Hiero hierarchical model, but the target side is a dependency structure. Only "well-formed" target structures are allowed: fixed structures with a known head, and floating structures, which are sibling sets under an unspecified head (for example "the red" as a modifier of a noun). Because of this restriction, the rule set is about 80% smaller than the replicated Hiero baseline (27M rules against 140M). The main gain comes from a target dependency language model, a trigram model over heads and their children, which is applied bottom-up during decoding. On NIST MT04 Chinese-English it improves BLEU by 1.48 and TER by 2.53 over the baseline with a 3-gram LM, and by 1.21 BLEU after 5-gram rescoring.

Ideas

• I1. Encoder output as well-formed target trees: the encoder in tools/translate/src/profile.rs builds word strings, and the open problem 3 asks for trees. Give each encoded chunk a fixed or floating category, and count how often a chunk's head and children can be joined under that rule. Report the share per author, run twice.
• I2. A count-based dependency language model for the encoder: count (head, child, side) triples over the English UD trees that the standard was learned from, and score candidate orders of the children of a head. Keep the tables in BTreeMap, and use them to choose the order when en/src/lin.rs has several equal-score chains.
• I3. Floating groups for function words: the paper handles "the red" as a floating sibling set. Try the same for determiners and "that" in the meta-world (open problem 4), so that an encoder can place them without a head.
• I4. Control: report the size of the chunk table before and after a well-formedness filter, and the exact-match change on dev. The paper's own control is the filtered string-to-string model, which loses the gain. The translator should have the same control, the standard with the filter but without the dependency score.

For Mova

The code is tools/translate/src/profile.rs (the encoder) and en/src/lin.rs (word order). The paper's decoder is not for Mova, since the translator has no search over many derivations at test time. The useful parts are the well-formedness check and the head-child count model, both count-based and deterministic.

Topics: Machine translation

Cite

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

@inproceedings{shen2008new,
  title = {{A New String-to-Dependency Machine Translation Algorithm with a Target Dependency Language Model}},
  author = {Shen, Libin and Xu, Jinxi and Weischedel, Ralph},
  booktitle = {Proceedings of ACL-08: HLT},
  year = {2008},
  url = {https://aclanthology.org/P08-1066/}
}

References

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