Learning a Multi-Domain Curriculum for Neural Machine Translation

2019/08/28 by Wei Wang, Ye Tian, Wang, Wei +9
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Software Engineering Research #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1908.10940

openalex publication_date 2019/08/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Taking the Human Out of the Loop: A Review of Bayesian Optimization
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Related

• Curriculum Learning for Domain Adaptation in Neural Machine Translation
• Multi-Domain Neural Machine Translation
• Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey
• Data Selection Curriculum for Neural Machine Translation
• Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation