This dataset derives from BOUQuET and WMT24++, both translation evaluation benchmarks. It is gated so that its contents are not picked up by web crawlers and absorbed into language-model training data. The terms below are BOUQuET's own, retained here as its licence requires. One scope note on the first term: it does not extend to datasets/smol/, which derives from SMOL rather than from a test set and is the training data for the Appendix I experiment.

Tailoring MT to Audience and Intent: data release

Data accompanying "Beyond 'To whom it may concern': Tailoring Machine Translation to Audience and Intent" (EMNLP 2026).

Paper: https://arxiv.org/abs/2606.03259
Code: https://github.com/raphaelmerx/purpose-mt

Why this is gated

The user instructions here are generated from BOUQuET and WMT24++ metadata and embed source text from both benchmarks. BOUQuET is distributed under terms that forbid re-hosting it where web crawlers can reach it, and ask users to keep its contents out of training data. WMT24++ is Apache-2.0 and could legally be posted openly, but it is also a test set, so we apply the same protection to it.

This is why the data lives here rather than in the GitHub repository, and why the three gate terms above are BOUQuET's own, carried over verbatim.

One scope note: datasets/smol/ derives from SMOL (CC-BY-4.0), not from a test set. It is the training data for the Appendix I distillation experiment, so the no-training term does not extend to it.

Paths mirror the layout the code expects, so python -m data.download in the GitHub repository places every file where the scripts look for it.

`datasets/`: generated user instructions

File	Rows	Paper
`bouquet_instructions_dev.jsonl`	504	§3.2, main results
`bouquet_instructions_test.jsonl`	854	held-out split, few-shot retrieval pool
`bouquet_instructions_dev_context.jsonl`	504	Appendix G, context-only ablation
`bouquet_instructions_dev_purpose.jsonl`	504	Appendix G, purpose-only ablation
`bouquet_instructions_dev_self-para-gemma-3-27b-it.jsonl`	504	§5, self-instruction
`bouquet_instructions_dev_self-para-gemma-4-31b-it.jsonl`	504	§5, self-instruction
`bouquet_instructions_test_self-para-gemma-3-27b-it.jsonl`	854	§5, self-instruction
`wmt24pp_instructions.jsonl`	997	Appendix C
`wmt24pp_instructions_self-para.jsonl`	997	Appendix C, self-instruction
`smol/en_sources.jsonl`	7,815	Appendix I, SMOL sources
`smol/en_instructions.jsonl`	7,815	Appendix I, instructions for distillation

BOUQuET instruction schema: uniq_id, tgt_text (English source), domain, par_comment, tags, register, user_instruction. Every field except user_instruction comes from BOUQuET; user_instruction is drafted by Gemini-3-Flash from that metadata and then revised by a human annotator.

`annotation_exports/`: human and LLM-judge evaluations

human/ holds 160 rated items per language for French, Indonesian, Ukrainian, Khmer and Javanese: error spans, a 0-100 ESA translation rating, and a 0-100 adaptedness score. These are the five annotation projects reported in the paper; earlier pilot projects are not included.

llm/ holds the LLM-judge scores over the same items, produced by human_eval.match_judge, which re-judges the exported text itself rather than joining on uniq_id. The _refbased files are the reference-based judge run behind the reference-free vs reference-based comparison in §3.4.

comet/ holds XCOMET-XL scores computed the same way, on exactly the translations the annotators rated (human_eval.match_comet). This matters: joining human ratings to the full test-set metric files on uniq_id pairs 38% of rated rows with a score computed on a different translation, because the test-set translations were regenerated after the annotations were collected. human_eval.human_metric_correlation --matched uses these files and recomputes ChrF++ from the export, so judge and metric are measured on identical text. Coverage: French covers 100 of its 160 rated rows; the other four languages are complete.

Annotators are identified only by an integer annotator_id. Free-text annotator_comment fields are linguistic notes and contain no personal data.

`scores/`: aggregated results

The per-condition means behind every table and figure, produced by python -m analysis.aggregate. No benchmark text; these are the numbers the paper reports. See the GitHub README for the column definitions.

`translation_results/controlled_mt/`: controlled-MT scores

Aggregate CoCoA-MT and MT-GenEval scores (Appendix K): M-Acc, coverage, commit rate, gender accuracy. No source text.

Not included

Item-level model outputs and judge evaluations (~770 MB) are not released: they embed large amounts of benchmark source text, and the aggregates in scores/ carry every number the paper reports. They are reproducible from the pipeline in the GitHub repository.

Licence and attribution

Our contributions, meaning the generated instructions, the human annotations and the aggregated scores, are released under CC-BY-4.0.

Derived from, and subject to the terms of, the following:

BOUQuET (facebook/bouquet), CC-BY-4.0, gated
WMT24++ (google/wmt24pp), Apache-2.0
SMOL (google/smol), CC-BY-4.0
CoCoA-MT, CDLA-Sharing-1.0
MT-GenEval, CC-BY-SA-3.0

Citation

@inproceedings{merx2026beyond,
  title     = {Beyond ``To whom it may concern'': Tailoring Machine Translation to Audience and Intent},
  author    = {Merx, Raphael and Vylomova, Ekaterina and Cohn, Trevor},
  booktitle = {Proceedings of EMNLP 2026},
  year      = {2026}
}

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Paper for raphaelmerx/purpose-mt

Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent

Paper • 2606.03259 • Published Jun 2

Protecting the integrity of these evaluation benchmarks

Tailoring MT to Audience and Intent: data release

Why this is gated

Contents

datasets/: generated user instructions

annotation_exports/: human and LLM-judge evaluations

scores/: aggregated results

translation_results/controlled_mt/: controlled-MT scores