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Datasets:
raphaelmerx
/
purpose-mt

Tasks:
Translation
Modalities:
Text
Formats:
json
Languages:
English
French
Indonesian
Size:
1K - 10K
ArXiv:
Tags:
machine-translation
instruction-following
llm-as-judge
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files
xet
 Community
1

Protecting the integrity of these evaluation benchmarks

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.

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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).

Contents

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.

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 data/README.md in the GitHub repository 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.

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:

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 the 2026 Conference on Empirical Methods in Natural Language Processing},
  year      = {2026}
}
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