Recovering document annotations for sentence-level bitext

• URL: http://arxiv.org/abs/2406.03869v1
• Date: Thu, 6 Jun 2024 08:58:14 GMT
• Title: Recovering document annotations for sentence-level bitext
• Authors: Rachel Wicks, Matt Post, Philipp Koehn,
• Abstract summary: We reconstruct document-level information for three datasets in German, French, Spanish, Italian, Polish, and Portuguese. We introduce a document-level filtering technique as an alternative to traditional bitext filtering. Last we train models on these longer contexts and demonstrate improvement in document-level translation without degradation of sentence-level translation.
• Score: 18.862295675088056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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