ZurichNLP/document-level-word-alignment
Document-Level Word Alignment Document-level word alignment data for six language pairs — English–French (en-fr), English–Romanian (en-ro), English–Japanese (en-ja), English–Chinese (en-zh), Latin–Ancient Greek (la-gr), and English–Czech (en-cz) — reconstructed from existing sentence-level, human-annotated word alignment gold standards. Document-level examples are built by grouping sentence-level annotations by document membership and sentence order; the dataset does not contain… See the full description on the dataset page: https://huggingface.co/datasets/ZurichNLP/document-level-word-alignment.
Document-Level Word Alignment
Document-level word alignment data for six language pairs — English–French (en-fr), English–Romanian (en-ro), English–Japanese (en-ja), English–Chinese (en-zh), Latin–Ancient Greek (la-gr), and English–Czech (en-cz) — reconstructed from existing sentence-level, human-annotated word alignment gold standards.
Document-level examples are built by grouping sentence-level annotations by document membership and sentence order; the dataset does not contain new human annotation beyond what already exists in the source corpora (see Sources & Licensing). This dataset accompanies the paper *"Scaling Unsupervised Word Alignment to Documents with Structural Constraints"*.
Dataset Structure
Each configuration corresponds to a language pair, e.g. en-ja. Each row is one document pair. Splits are dev and test, except en-cz, which is test-only.
Data Fields
Example
{"sent_id": "trial", "doc_id": "trial", "lang": "en-fr",
"text_a": "source document text ...", "text_b": "target document text ...",
"sure_align": "0-0 3-5", "possible_align": "1-1"}Dataset Statistics (dev / test)
Lengths are target-side (text_b) character counts.
Sources & Licensing
This is a derived/reconstructed dataset. Each language pair originates from a separate, independently licensed source.
Dataset Creation
Document-level examples were constructed by concatenating sentence-level source/target texts within a document in their original order and re-indexing sure_align/possible_align accordingly.
Citation
If you use this dataset, please cite both this work and the original source(s) listed in Sources & Licensing for the language pair(s) you use.
@misc{wastl2026scalingunsupervisedwordalignment,
title={Scaling Unsupervised Word Alignment to Documents via Structural Constraints},
author={Michelle Wastl and Jannis Vamvas and Rico Sennrich},
year={2026},
eprint={2608.21023},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2608.21023},
}