Domain Adaptation of Machine Translation with Crowdworkers

• URL: http://arxiv.org/abs/2210.15861v1
• Date: Fri, 28 Oct 2022 03:11:17 GMT
• Title: Domain Adaptation of Machine Translation with Crowdworkers
• Authors: Makoto Morishita, Jun Suzuki, Masaaki Nagata
• Abstract summary: We propose a framework that efficiently 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.
• Score: 34.29644521425858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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