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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.</abstract>
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%0 Conference Proceedings
%T Learning a Multi-Domain Curriculum for Neural Machine Translation
%A Wang, Wei
%A Tian, Ye
%A Ngiam, Jiquan
%A Yang, Yinfei
%A Caswell, Isaac
%A Parekh, Zarana
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-learning-multi
%X 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.
%R 10.18653/v1/2020.acl-main.689
%U https://aclanthology.org/2020.acl-main.689
%U https://doi.org/10.18653/v1/2020.acl-main.689
%P 7711-7723

Markdown (Informal)

[Learning a Multi-Domain Curriculum for Neural Machine Translation](https://aclanthology.org/2020.acl-main.689) (Wang et al., ACL 2020)

• Learning a Multi-Domain Curriculum for Neural Machine Translation (Wang et al., ACL 2020)

ACL

• Wei Wang, Ye Tian, Jiquan Ngiam, Yinfei Yang, Isaac Caswell, and Zarana Parekh. 2020. Learning a Multi-Domain Curriculum for Neural Machine Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7711–7723, Online. Association for Computational Linguistics.