README.md

---
license: other
license_name: wmt-research-terms
license_link: https://www.statmt.org/wmt19/translation-task.html
task_categories:
- translation
language:
- en
- zh
- de
tags:
- entropy-valley
- machine-translation
- masked-diffusion
- diffusion-language-model
- wmt
- length-prediction
- arxiv:2608.22274
pretty_name: Entropy-Valley Datasets
size_categories:
- 100K<n<1M
configs:
- config_name: enzh
  data_files:
  - split: train
    path: enzh_train.jsonl
  - split: validation
    path: enzh_dev.jsonl
  - split: test
    path: wmt22_enzh_test.jsonl
- config_name: ende
  data_files:
  - split: train
    path: ende_train.jsonl
  - split: validation
    path: ende_dev.jsonl
  - split: test
    path: wmt22_ende_test.jsonl
- config_name: challenge_coverage_zh
  data_files:
  - split: test
    path: challenge_sets/challenge_coverage_zh.jsonl
- config_name: challenge_numbers
  data_files:
  - split: test
    path: challenge_sets/challenge_numbers.jsonl
- config_name: challenge_named_entities
  data_files:
  - split: test
    path: challenge_sets/challenge_named_entities.jsonl
- config_name: challenge_dates
  data_files:
  - split: test
    path: challenge_sets/challenge_dates.jsonl
- config_name: challenge_enumeration
  data_files:
  - split: test
    path: challenge_sets/challenge_enumeration.jsonl
- config_name: challenge_fertility_mismatch
  data_files:
  - split: test
    path: challenge_sets/challenge_fertility_mismatch.jsonl
---

# Entropy-Valley Datasets

[**📄 Paper (arXiv:2608.22274)**](https://arxiv.org/abs/2608.22274) | [**💻 GitHub**](https://github.com/Entropy-Valley/Entropy-Valley) | [**🤗 Models**](https://huggingface.co/collections/YanZhanPKU/entropy-valley)

This repository contains every data file read by **Entropy-Valley (EV)**, the training-free target-length selector for masked diffusion machine translation introduced in *"Length-Adaptive Decoding for Masked Diffusion Machine Translation"* (EMNLP 2026 Main Conference).

Masked diffusion language models decode by filling a **fixed-size canvas**, so the target length must be chosen *before* denoising starts. EV probes $K{=}5$ candidate canvas lengths with one all-mask forward pass each and picks the one with the lowest mean predictive entropy. These files are the LoRA-SFT training data, the WMT22 evaluation sets, and the diagnostic challenge subsets used to measure that decision.

## Dataset Summary

| Config | Split | Rows | Fields | Role in the paper |
|---|---|---:|---|---|
| `enzh` | train | 200,000 | `en`, `zh` | LoRA-SFT data for **both** En→Zh and Zh→En |
| `enzh` | validation | 2,000 | `en`, `zh` | Held-out dev split (not used to tune EV) |
| `enzh` | test | 2,037 | `en`, `zh` | Official WMT22 News test set; main results table |
| `ende` | train | 200,000 | `en`, `de` | LoRA-SFT data for the matched-scale En→De check |
| `ende` | validation | 2,000 | `en`, `de` | Held-out dev split |
| `ende` | test | 2,037 | `en`, `de` | Official WMT22 News test set (En→De) |
| `challenge_coverage_zh` | test | 400 | `en`, `zh`, `challenge_categories`, `idx` | Union subset for the reveal-order diagnostic |
| `challenge_numbers` | test | 200 | `en`, `zh`, `challenge_category`, `idx` | Sentences containing digit runs |
| `challenge_named_entities` | test | 200 | `en`, `zh`, `challenge_category`, `idx` | Capitalised multi-token spans |
| `challenge_dates` | test | 63 | `en`, `zh`, `challenge_category`, `idx` | Year / month / weekday tokens |
| `challenge_enumeration` | test | 131 | `en`, `zh`, `challenge_category`, `idx` | Comma-separated lists of ≥3 items |
| `challenge_fertility_mismatch` | test | 14 | `en`, `zh`, `challenge_category`, `idx` | Source–target length-ratio outliers |

Every file is JSONL: one example per line, one JSON object per line, no nesting.

> **Note on direction.** `Zh→En` reuses the same `enzh` files — `lang_pair: zh-en` in the code simply swaps which key is source and which is target. There is no separate `zhen` config.

## Construction Protocol

### Training and dev splits

Built from the HuggingFace `wmt19` corpus with `ladit/data/prepare_mt_data*.py`:

- **En↔Zh** — `load_dataset("wmt19", "zh-en", split="train")`. Filter: `5 <= len(en.split()) <= 200` and `2 <= len(zh) <= 600` (English whitespace words, Chinese characters).
- **En→De** — `load_dataset("wmt19", "de-en", split="train")`, shuffled with `seed=42` before iteration. Filter: `5 <= len(en.split()) <= 200` and `5 <= len(de.split()) <= 200`.

After filtering, pairs are shuffled deterministically (`random.seed(42)` in the released scripts); the first 200,000 become the train split and the next 2,000 become the dev split.

**Only single-side length filters are applied.** No language-ID classifier and no source–target length-ratio filter.

**What the dev split is *not* used for.** The WMT19 development pairs are not used to set the reported EV ratio grids or any decoding hyperparameter. The grids reported in the paper are direction-specific and were informed by diagnostic range comparisons on WMT22 subsets; the WMT19 training-corpus medians (0.80 / 1.24 / 1.48) serve only as a reference scale. Once chosen, a grid is fixed and used for every sentence and every length method in that direction.

### Test sets

The official WMT22 News Translation test sets, $N{=}2{,}037$ per direction.

- **En↔Zh** — sources and references from [wmt-conference/wmt22-news-systems](https://github.com/wmt-conference/wmt22-news-systems/tree/main/txt), combined into the JSONL schema above. En→Zh and Zh→En reuse the same file with roles swapped.
- **En→De** — fetched via sacreBLEU's `get_source_file("wmt22", "en-de")` and `get_reference_files("wmt22", "en-de")`.

### Challenge subsets

Derived from `wmt22_enzh_test.jsonl` by `ladit/data/build_challenge_sets.py` with `--max_per_category 200`. Subsets are **not** mutually exclusive: `challenge_coverage_zh` is the 400-sentence union used for the reveal-order diagnostic, and each per-category file carries its trigger in `challenge_category`. `idx` is the 0-based row index into `wmt22_enzh_test.jsonl`, so every challenge example can be traced back to its source row.

Note that the per-category counts here are the files as built with the 200-cap; the paper's reveal-order table reports the category composition *within* the 400-sentence union subset, which is smaller for the capped categories.

## Usage

```python
from datasets import load_dataset

enzh  = load_dataset("YanZhanPKU/Entropy-Valley-Datasets", "enzh")
ende  = load_dataset("YanZhanPKU/Entropy-Valley-Datasets", "ende")
nums  = load_dataset("YanZhanPKU/Entropy-Valley-Datasets", "challenge_numbers", split="test")

print(enzh)
# DatasetDict({train: 200000, validation: 2000, test: 2037})
print(enzh["test"][0])
# {'en': "I'm sorry that your order is running late.", 'zh': '很抱歉，您点的餐可能会晚到一会。'}
```

Or download the raw JSONL tree, which is the layout the released code expects:

```bash
huggingface-cli download YanZhanPKU/Entropy-Valley-Datasets --repo-type dataset --local-dir ./data
```

Then run the full pipeline from the [code repository](https://github.com/Entropy-Valley/Entropy-Valley):

```bash
bash scripts/train_and_eval.sh configs/enzh.yaml 42
```

## Rebuilding from Source

Nothing here is hand-annotated — every file is reproducible from the public WMT releases:

```bash
python -m ladit.data.prepare_mt_data      --output_dir data --num_train 200000 --seed 42
python -m ladit.data.prepare_mt_data_ende --output_dir data --num_train 200000 --seed 42
python -m ladit.data.build_challenge_sets --input data/wmt22_enzh_test.jsonl \
    --outdir data/challenge_sets --max_per_category 200
```

## Licensing and Provenance

The paper's *Resource availability* paragraph (Appendix A) states that the processed experiment datasets and trained LoRA adapters are released here; this repository is that release.

These files are **derived subsets** of the public WMT19 training corpora and the public WMT22 News Translation test sets, redistributed here in a reformatted JSONL layout so that the paper's results are reproducible without re-running the sampling pipeline. They are **not** a new corpus, and no raw WMT distribution is mirrored in full.

Use is subject to the original WMT terms, which permit use for machine-translation research. Source references:

- WMT19 translation task: <https://www.statmt.org/wmt19/translation-task.html> (HF mirror: [`wmt/wmt19`](https://huggingface.co/datasets/wmt/wmt19))
- WMT22 news systems and test sets: <https://github.com/wmt-conference/wmt22-news-systems>

If you are a rights holder for any WMT19 sub-corpus and want a portion removed, please open an issue on the [code repository](https://github.com/Entropy-Valley/Entropy-Valley/issues).

**Privacy.** The WMT news benchmarks are public and may contain names or events already present in public news text. No private user data was collected, and no attempt was made to identify individuals.

## Citation

```bibtex
@inproceedings{zhan2026lengthadaptive,
  title         = {Length-Adaptive Decoding for Masked Diffusion Machine Translation},
  author        = {Zhan, Yan and Hou, Mengkai and Zhang, Wanting and Gao, Zhijun},
  booktitle     = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year          = {2026},
  eprint        = {2608.22274},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2608.22274}
}
```

Please also cite the underlying WMT shared tasks:

```bibtex
@inproceedings{barrault-etal-2019-findings,
  title     = {Findings of the 2019 Conference on Machine Translation (WMT19)},
  author    = {Barrault, Lo{\"i}c and Bojar, Ond{\v{r}}ej and Costa-juss{\`a}, Marta R. and others},
  booktitle = {Proceedings of the Fourth Conference on Machine Translation},
  year      = {2019}
}

@inproceedings{kocmi-etal-2022-findings,
  title     = {Findings of the 2022 Conference on Machine Translation (WMT22)},
  author    = {Kocmi, Tom and Bawden, Rachel and Bojar, Ond{\v{r}}ej and others},
  booktitle = {Proceedings of the Seventh Conference on Machine Translation},
  year      = {2022}
}
```