A Survey of Domain Adaptation for Neural Machine Translation

2018/06/01 by Chenhui Chu, Rui Wang, Chu, Chenhui +1 · 7 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1806.00258

openalex publication_date 2018/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Neural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although the high-quality and domain-specific translation is crucial in the real world, domain-specific corpora are usually scarce or nonexistent, and thus vanilla NMT performs poorly in such scenarios. Domain adaptation that leverages both out-of-domain parallel corpora as well as monolingual corpora for in-domain translation, is very important for domain-specific translation. In this paper, we give a comprehensive survey of the state-of-the-art domain adaptation techniques for NMT.

Citations

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Domain Adaptation for Visual Applications: A Comprehensive Survey
• Multiple Source Domain Adaptation with Adversarial Training of Neural\n Networks
• Adversarial Discriminative Domain Adaptation
• Multi-Adversarial Domain Adaptation

Cited by

• BanglaSTEM: A Parallel Corpus for Technical Domain Bangla-English Translation
• Data-Efficient Domain Adaptation for LLM-based MT using Contrastive Preference Optimization
• Literature review on vulnerability detection using NLP technology
• On the Language Coverage Bias for Neural Machine Translation
• Neural Machine Translation with Monolingual Translation Memory
• A Survey on Low-Resource Neural Machine Translation
• Using Machine Translation to Localize Task Oriented NLG Output

Related

• Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey
• An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation
• Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation
• Fast Domain Adaptation for Neural Machine Translation
• Multilingual Multi-Domain Adaptation Approaches for Neural Machine Translation