README.md

metadata

license: cc-by-4.0
language:
  - en
  - fr
  - id
  - uk
  - km
  - jv
task_categories:
  - translation
tags:
  - machine-translation
  - instruction-following
  - llm-as-judge
extra_gated_heading: Protecting the integrity of these evaluation benchmarks
extra_gated_description: >-
  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.
extra_gated_fields:
  This data is for evaluation purposes only; You may not use any of this data or its derivatives for training machine learning / AI models: checkbox
  You may only distribute, embed, or otherwise transfer this data or its derivatives via a mechanism that is either private or that implements protections against automated crawling (such as using a password-protected archive or a gating mechanism that requires users to accept these terms before accessing the dataset): checkbox
  Your distributions must retain these terms: checkbox

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

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:

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