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Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation

What it is about

Translated English has statistical habits that original English lacks, which the field calls translationese. The paper frames a task: make translated sentences look more like originally written English, without losing meaning. Three methods are tried on 1,000 translated sentences from a Europarl-based corpus, and compared with 1,000 native sentences. Two paraphrase models, one T5-based and one BART-based, are one method. Round-trip machine translation through French is the second. The third is parse-then-generate through AMR. Three macro measures are used: type-token ratio, the count of cohesive markers, and the unigram distribution of part-of-speech tags. The original translations have a type-token ratio of 0.0890 and 461 cohesive markers. The AMR route moves these to 0.1002 and 348, and it is the only method that improves every measure without hurting fluency or adequacy. The BART paraphraser moves the numbers further but over-summarises. The round-trip route makes things worse.

Ideas

For Mova

This paper touches open problem 6: the standard leans to the Bibles, so archaic English should be learned as a delta, and weighting corpora one vote each would help. The measures in I1 to I3 belong in tools/translate/src/measure.rs. The per-author delta is in profile.rs.

Topics: Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.eacl-long.45

@inproceedings{wein2024lost,
  title = {{Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation}},
  author = {Wein, Shira and Schneider, Nathan},
  booktitle = {Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2024},
  doi = {10.18653/v1/2024.eacl-long.45},
  url = {https://doi.org/10.18653/v1/2024.eacl-long.45}
}

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