Character-Based Pivot Translation for Under-Resourced Languages and Domains

In this paper we investigate the use of character-level translation models to support the translation from and to under-resourced languages and textual domains via closely related pivot languages. Our experiments show that these low-level models can be successful even with tiny amounts of training data. We test the approach on movie subtitles for three language pairs and legal texts for another language pair in a domain adaptation task. Our pivot translations outperform the baselines by a large margin.

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