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arXiv · 1908.10940

Learning a Multi-Domain Curriculum for Neural Machine Translation

Wei Wang·Ye Tian·Jiquan Ngiam·Yinfei Yang·Isaac Caswell·Zarana Parekh
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Abstract

Most data selection research in machine translation focuses on improving a single domain. We perform data selection for multiple domains at once. This is achieved by carefully introducing instance-level domain-relevance features and automatically constructing a training curriculum to gradually concentrate on multi-domain relevant and noise-reduced data batches. Both the choice of features and the use of curriculum are crucial for balancing and improving all domains, including out-of-domain. In large-scale experiments, the multi-domain curriculum simultaneously reaches or outperforms the individual performance and brings solid gains over no-curriculum training.

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Wei Wang, Ye Tian, Jiquan Ngiam, Yinfei Yang, Isaac Caswell, Zarana Parekh. 2019-08-28. Learning a Multi-Domain Curriculum for Neural Machine Translation. https://arxiv.org/abs/1908.10940

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