A Challenge Set Approach to Evaluating Machine Translation

What it is about

The paper proposes challenge sets: small, hand-written sets of English to French sentences, each built to test one structural divergence between the two languages, for example morphology that exposes agreement, stranded prepositions, double-object verbs, subcategorisation frames, idioms and WH-movement. Each item has a yes or no question for a human judge. The authors compare phrase-based and neural systems of 2017. Neural systems beat phrase-based ones on almost every category, idioms being the exception. Some rules are learned by the neural system in simple cases and still fail in longer sentences, which the authors report as brittle generalisation. The method is manual, the sets are small, and native speakers are needed to repeat it.

Ideas

• I1. A challenge set for the Bible translator: one set per divergence between Hebrew and English (verb-initial order, construct chains, thou and ye by number and person, hath, negation particles, idioms). Each item has a fixed target output, and the score is the exact-match rate per class, not BLEU.
• I2. Turn the yes or no questions into rules a script can check on the output (for example, the subject precedes the verb). The set then needs no human judge, and it runs in the test suite.
• I3. A brittleness item: a pattern that passes in the short form and fails in a longer verse, to test the frame and encoder path rather than the lexicon.

For Mova

The places are tools/translate test data, tools/macula for the source trees and en/src/morph.rs for the forms. The items are small and can live in the repo as data. The set is a dev instrument; any score on it must be read against the held-out rule and the two-run check.

Topics: Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/d17-1263

@inproceedings{isabelle2017challenge,
  title = {{A Challenge Set Approach to Evaluating Machine Translation}},
  author = {Isabelle, Pierre and Cherry, Colin and Foster, George},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  year = {2017},
  doi = {10.18653/v1/d17-1263},
  url = {https://doi.org/10.18653/v1/d17-1263}
}

References

16 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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3. Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurelie Neveol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, and Marcos Zampieri. 2016. Findings of the 2016 conference on machine translation. In Proceedings of the First Conference on Machine Translation. Association for Computational Linguistics, Berlin, Germany, pages 131–198. http://www.aclweb.org/anthology/W16-2301. ↗
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5. Colin Cherry and George Foster. 2012. Batch tuning strategies for statistical machine translation. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Montréal, Canada, pages 427–436. http://www.aclweb.org/anthology/N12-1047. ↗
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