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arXiv · 2210.15861

Domain Adaptation of Machine Translation with Crowdworkers

Makoto Morishita·Jun Suzuki·Masaaki Nagata
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Abstract

Although a machine translation model trained with a large in-domain parallel corpus achieves remarkable results, it still works poorly when no in-domain data are available. This situation restricts the applicability of machine translation when the target domain's data are limited. However, there is great demand for high-quality domain-specific machine translation models for many domains. We propose a framework that efficiently and effectively collects parallel sentences in a target domain from the web with the help of crowdworkers. With the collected parallel data, we can quickly adapt a machine translation model to the target domain. Our experiments show that the proposed method can collect target-domain parallel data over a few days at a reasonable cost. We tested it with five domains, and the domain-adapted model improved the BLEU scores to +19.7 by an average of +7.8 points compared to a general-purpose translation model.

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Makoto Morishita, Jun Suzuki, Masaaki Nagata. 2022-10-28. Domain Adaptation of Machine Translation with Crowdworkers. https://arxiv.org/abs/2210.15861

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