MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation.

Area of Science:

• Natural Language Processing
• Machine Learning
• Computational Linguistics

Background:

• Neural Machine Translation (NMT) models excel with large datasets but struggle with domain-specific translation due to data scarcity.
• Adapting NMT to specialized domains with limited data remains a significant challenge in the field.

Purpose of the Study:

• To develop a novel NMT model capable of rapid domain adaptation for low-resource scenarios.
• To enhance NMT generalization across diverse domains using a new word embedding transition technique.

Main Methods:

• Proposed a novel NMT model that splits parameters into model and meta parameters.
• Introduced a meta-learning training strategy to alternately update model and meta parameters.
• Mimicked domain adaptation to low-resource domains via multiple translation tasks.

Main Results:

• Demonstrated substantial improvements in NMT performance on limited domain-specific data.
• The proposed model effectively generalized to different domains with scarce training examples.
• The word embedding transition technique facilitated fast domain adaptation.

Conclusions:

• The novel NMT model with meta-learning and parameter splitting significantly enhances translation quality in low-resource domains.
• This approach offers an effective solution for domain adaptation in machine translation.
• Future work can explore further optimizations of the meta-learning strategy for NMT.