Curriculum Learning for Domain Adaptation in Neural Machine Translation

05/14/2019

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We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs.

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Curriculum Learning for Domain Adaptation in Neural Machine Translation

Domain specialization: a post-training domain adaptation for Neural Machine Translation

Curriculum Learning and Minibatch Bucketing in Neural Machine Translation

An Empirical Exploration of Curriculum Learning for Neural Machine Translation

Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation

Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation

Compact Personalized Models for Neural Machine Translation

Dynamically Composing Domain-Data Selection with Clean-Data Selection by "Co-Curricular Learning" for Neural Machine Translation

Curriculum Learning for Domain Adaptation in Neural Machine Translation

Related Research

Domain specialization: a post-training domain adaptation for Neural Machine Translation

Curriculum Learning and Minibatch Bucketing in Neural Machine Translation

An Empirical Exploration of Curriculum Learning for Neural Machine Translation

Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation

Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation

Compact Personalized Models for Neural Machine Translation

Dynamically Composing Domain-Data Selection with Clean-Data Selection by "Co-Curricular Learning" for Neural Machine Translation