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Modelling pronominal anaphora in statistical machine translation

Summary

Standard SMT systems translate each sentence in isolation, so they cannot use earlier context. This is a real problem for pronouns, whose correct form often depends on an antecedent that may appear in another sentence. Hardmeier and Federico first measure the problem. They manually checked how their phrase-based WMT 2010 system rendered German anaphoric pronouns into English in 13 documents from the WMT 2009 newswire test set. Only about three in five pronouns were translated acceptably. Feminine, polite-address and reflexive forms did worst, mainly because German forms such as sie and sich are ambiguous. The authors then propose a word dependency model that scores links between pairs of words, either inside one sentence or across sentence boundaries. For English-German translation, they feed pronoun–antecedent links from the BART coreference resolver into this model, so the output pronoun can agree with how the antecedent's head was translated. This yields a small but statistically significant gain in pronoun translation recall without hurting other aspects of quality. It is an early step toward SMT that treats a text as connected discourse.

Key points

  • The case study covered 13 documents (219 sentences) from the WMT 2009 German-English test set, which has 111 newswire documents. Overall, 122 of 199 anaphoric pronouns (61 %) were translated adequately by a system using 6-gram language models.
  • Accuracy varied strongly by pronoun type:
    • demonstratives: about 90 %
    • masculine/neuter singular and plural: about 77 %
    • feminine singular: 33 %
    • reflexive: 27 %
    • polite address: 25 %
  • The experiments ran in the English-German direction because an English coreference resolver was available. BART, trained on the ACE02-npaper corpus with separate pronoun models, links each pronoun to the syntactic head of one antecedent noun phrase.
  • The only prior SMT work cited is Le Nagard and Koehn's English-French approach. It left BLEU unchanged, and correct pronouns dropped slightly from 69 % to 68 %.
  • Planned future work includes:
    • easier language pairs (English-French, German-English)
    • manually annotated coreference to measure noise
    • reusing the dependency model for verb and adjective agreement

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Coreference resolution · Machine translation

Cited by

76 papers of our library cite it (of the 140673 whose bibliographies we read); 15 of them have a page here, 61 not yet.

Cite

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

@inproceedings{hardmeier2010modelling,
  title = {{Modelling pronominal anaphora in statistical machine translation}},
  author = {Hardmeier, Christian and Federico, Marcello},
  booktitle = {Proceedings of the 7th International Workshop on Spoken Language Translation: Papers},
  year = {2010},
  url = {https://aclanthology.org/2010.iwslt-papers.10/}
}

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