Recovering document annotations for sentence-level bitext
<|reference_start|>Recovering document annotations for sentence-level bitext: Data availability limits the scope of any given task. In machine translation, historical models were incapable of handling longer contexts, so the lack of document-level datasets was less noticeable. Now, despite the emergence of long-sequence methods, we remain within a sentence-level paradigm and without data to adequately approach context-aware machine translation. Most large-scale datasets have been processed through a pipeline that discards document-level metadata. In this work, we reconstruct document-level information for three (ParaCrawl, News Commentary, and Europarl) large datasets in German, French, Spanish, Italian, Polish, and Portuguese (paired with English). We then introduce a document-level filtering technique as an alternative to traditional bitext filtering. We present this filtering with analysis to show that this method prefers context-consistent translations rather than those that may have been sentence-level machine translated. Last we train models on these longer contexts and demonstrate improvement in document-level translation without degradation of sentence-level translation. We release our dataset, ParaDocs, and resulting models as a resource to the community.<|reference_end|>
@article{wicks2024recovering,
    title={Recovering document annotations for sentence-level bitext},
    author={Rachel Wicks and Matt Post and Philipp Koehn},
    journal={arXiv preprint arXiv:2406.03869},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03869},
    primaryClass={cs.CL}
}