---
title: "When the Gold Standard Isn't Necessarily — Nishimwe 2026"
description: "Machine translation of user-generated content (UGC) is hard to evaluate."
authors: ["Lydia Nishimwe", "Benoît Sagot", "Rachel Bawden"]
year: 2026
venue: "Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)"
source: "https://aclanthology.org/2026.eamt-1.30/"
licence: "CC BY-ND 4.0 (EAMT)"
licence_url: "https://creativecommons.org/licenses/by-nd/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/nishimwe-2026-when-gold-standard-isn-t/"
---
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# When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Content

**Authors:** Lydia Nishimwe, [Benoît Sagot](https://md.research.elamur.ai/free/authors/benoit-sagot/), [Rachel Bawden](https://md.research.elamur.ai/free/authors/rachel-bawden/) · **Year:** 2026 · **Venue:** Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1) · **Licence:** [CC BY-ND 4.0 (EAMT)](https://creativecommons.org/licenses/by-nd/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eamt-1.30/) · [PDF](https://aclanthology.org/2026.eamt-1.30.pdf) · [OpenAlex](https://openalex.org/W7116842947)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
Machine translation of user-generated content (UGC) is hard to evaluate. Social media and forum posts are full of non-standard language, such as typos, slang, letter repetitions and emojis, and nobody agrees on how much of it should survive in the target text. The authors examine the human translation instructions behind four UGC parallel corpora: RoCS-MT, FooTweets, MMTC and PFSMB. These cover English–French, English–German and French–English. Drawing on these instructions, they classify twelve kinds of non-standard phenomena and define five possible ways of handling them in translation (NORMALISE, COPY, TRANSFER, OMIT, CENSOR). The corpora turn out to sit at different points on a standardness scale, from heavily normalising to preservation-oriented.

They then prompt several LLMs in three ways: with no guidelines, with each dataset's own guidelines, and with guidelines borrowed from another dataset. Metric scores react strongly to these instructions and improve when the prompt matches the guidelines behind the references. Reference-based metrics reward agreement with the reference style, while COMET-Kiwi prefers standardised output. Without documented guidelines that both systems and metrics take into account, UGC benchmark comparisons can be misleading.

## Key points
- **Datasets and pairs:** RoCS-MT is English–French and translates lexically normalised source text. FooTweets is English–German, built from 2014 FIFA World Cup tweets with sentiment analysis in mind. MMTC and PFSMB are French–English. MTNT and Foursquare were excluded because they document no translator guidelines.
- **Shared and divergent guidelines:** All four corpora correct grammar and spelling and copy emojis. They differ on other phenomena. For example, RoCS-MT normalises word elongation and atypical punctuation, whereas the other three transfer elongation and copy unusual punctuation.
- **Models tested:** The baseline was NLLB-3B, which cannot be prompted with guidelines. The instruction-tuned LLMs were Gemma-2-9B, Granite-4.1-8B, Mistral-0.3-7B, Qwen-2.5-7B, Tower-0.2-7B and LLaMA-3.1-8B.
  - Most LLMs adapted to guideline prompts.
  - Tower barely reacted to them.
  - LLaMA often refused "unsafe" inputs, which lowered its corpus-level scores.
- **Metrics:** BLEU and COMET (reference-based) penalise style mismatches with the references, for instance when minimally standardising prompts are scored against RoCS-MT. COMET-Kiwi (reference-less) gave lower scores to highly non-standard but guideline-faithful outputs on PFSMB.
- **Recommendations:**
  - If style does not matter, use reference-less metrics or multiple references at different standardness levels.
  - If a specific style is required, prompt the model with explicit guidelines and evaluate with a reference-based method, ideally an LLM-as-a-judge given the same guidelines.
  - Future human evaluation should use phenomenon-specific rubrics.
  - Code and guidelines are released on GitHub (lydianish-phd/ugc-mt-eval-challenges).

*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](https://md.research.elamur.ai/free/shelves/linguistics/) · [Machine translation](https://md.research.elamur.ai/free/shelves/machine-translation/)

## Cite

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

```bibtex
@inproceedings{nishimwe2026when,
  title = {{When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Content}},
  author = {Nishimwe, Lydia and Sagot, Benoît and Bawden, Rachel},
  booktitle = {Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)},
  year = {2026},
  url = {https://aclanthology.org/2026.eamt-1.30/}
}
```

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