The University of Helsinki submissions to the WMT19 news translation task
Abstract
The paper discusses the University of Helsinki's submissions for the WMT 2019 shared task, focusing on data cleaning, sentence-level transformer models, document-level translation approaches, and different segmentation methods for English-German, English-Finnish, and Finnish-English translation tasks.
In this paper, we present the University of Helsinki submissions to the WMT 2019 shared task on news translation in three language pairs: English-German, English-Finnish and Finnish-English. This year, we focused first on cleaning and filtering the training data using multiple data-filtering approaches, resulting in much smaller and cleaner training sets. For English-German, we trained both sentence-level transformer models and compared different document-level translation approaches. For Finnish-English and English-Finnish we focused on different segmentation approaches, and we also included a rule-based system for English-Finnish.
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