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# Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation

**Authors:** [Shira Wein](https://md.mova.elamur.ai/free/en/docs/papers/authors/shira-wein/), [Nathan Schneider](https://md.mova.elamur.ai/free/en/docs/papers/authors/nathan-schneider/) · **Year:** 2024 · **Venue:** EACL 2024 (Volume 1: Long Papers) **Link:** [aclanthology.org/2024.eacl-long.45](https://aclanthology.org/2024.eacl-long.45/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**2** citations](https://openalex.org/works?filter=cites:W4411630287) openalex · 2026-10-18[**14** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/wein-2024-lost-translationese-reducing-translation-effect/) of the 140673 papers whose bibliographies we read[Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)](https://aclanthology.org/volumes/2024.eacl-long/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2024.eacl-long.45/)
- [PDF](https://aclanthology.org/2024.eacl-long.45.pdf)
- [DOI 10.18653/v1/2024.eacl-long.45](https://doi.org/10.18653/v1/2024.eacl-long.45)
- [OpenAlex](https://openalex.org/W4411630287)

**Authors (2)**

1. [Shira Wein](https://openalex.org/A5075058724) [ORCID](https://orcid.org/0000-0002-1062-0866) · h 6 · 80 citations · 54 works
2. [Nathan Schneider](https://openalex.org/A5014008069) [ORCID](https://orcid.org/0000-0002-2745-0533) · h 28 · 6684 citations · 159 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## 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

- I1. Standard-only rendering. Render Mova output from the meta-world with the standard encoder and no author delta. Measure type-token ratio and connective counts against original English. This tests whether the standard by itself removes translationese.
- I2. Connectives as a measure. Translated text over-uses cohesive markers, and the AMR route lowers their count. Count discourse connectives in the translator's output, in its source and in original English of the same register. Report which connectives the meta-world drops, so a drop that loses sense is told apart from one that only removes translationese.
- I3. A vector, not one score. The round-trip result, which helps one metric and hurts another, argues for reporting all the measures together. Keep the counts in ordered maps so that the reports are identical across two runs.
- I4. Translated versus native as a style class. The paper's contrast between translated and native text is a ready signal for learning a style delta.

## 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](https://md.mova.elamur.ai/free/en/docs/papers/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](https://doi.org/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}
}
```

- [← previous Assessing the Cross-linguistic Utility of Abstract Meaning Representation](https://md.mova.elamur.ai/free/en/docs/papers/wein-2024-assessing-cross-linguistic-utility-abstract/)
- [next → Towards broad coverage surface realization with CCG](https://md.mova.elamur.ai/free/en/docs/papers/white-2007-broad-coverage-surface-realization-ccg/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

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