| --- |
| 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](https://huggingface.co/datasets/facebook/bouquet)), CC-BY-4.0, gated |
| - **WMT24++** ([google/wmt24pp](https://huggingface.co/datasets/google/wmt24pp)), Apache-2.0 |
| - **SMOL** ([google/smol](https://huggingface.co/datasets/google/smol)), CC-BY-4.0 |
| - **CoCoA-MT**, CDLA-Sharing-1.0 |
| - **MT-GenEval**, CC-BY-SA-3.0 |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
