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Crossroads between contrastive linguistics, translation studies and machine translation: TC3-II

What it is about

An edited volume from the TC3 workshop series on three fields that work with parallel and comparable corpora: contrastive linguistics, translation studies and machine translation. The chapters that matter for translation divergences are these. Thunes analyses English-Norwegian parallel texts (about 35,000 source word forms, 4,439 string pairs, a law text and a short story). Each pair is placed on a four-type correspondence hierarchy, from word-for-word correspondence (type 1) to non-computable correspondence (type 4). Type 4 covers half of all pairs, and the share of linguistically predictable pairs is 50.2% for law and 39.6% for fiction. Kremer and co-authors test whether statistical MT suggestions help non-professional translators with English adjective-noun choice. Support suggestions are rated as high quality, translators pick the best candidate, and ratings differ significantly between no support and unigram support. Zinsmeister, Dipper and Seiss compare abstract anaphors (this, dies, label nouns) in the Europarl corpus; translated German overuses dies, which the authors link to shining through of the English this. Korzen and Gylling compare information density and clause linkage in Italian and Danish Europarl. Carl and Dragsted model default and challenged translation production as a process. Steiner argues for shared empirical methods across the three fields.

Ideas

For Mova

The correspondence-type hierarchy is the most usable idea in the volume for tools/translate and tools/roundtrip: it gives a fixed label set (an enum) for the source of each residual bit, which makes the residual readable. The code touched is tools/translate/src/measure.rs (residual classes) and tools/roundtrip (the divergence classes). The shining-through counts relate to the author deltas in profile.rs. The rest (eye-tracking, human-rating studies, neural MT suggestions) does not carry over, except as a method for judging candidates.

Topics: Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.5281/zenodo.1019700

@book{czulo2017crossroads,
  title = {{Crossroads between contrastive linguistics, translation studies and machine translation: TC3-II}},
  author = {Czulo, Oliver and Hansen-schirra, Silvia},
  year = {2017},
  doi = {10.5281/zenodo.1019700},
  url = {https://doi.org/10.5281/zenodo.1019700}
}

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