Multilingual Multi-Domain Adaptation Approaches for Neural Machine Translation
This work addresses the problem of domain adaptation in neural machine translation for multilingual settings, offering incremental improvements over existing methods.
The paper tackles domain adaptation for neural machine translation by proposing two novel methods for training a single model across multiple domains, combined with multilingualism and mixed fine-tuning, resulting in significant improvements for both resource-poor in-domain and resource-rich out-of-domain translations.
In this paper, we propose two novel methods for domain adaptation for the attention-only neural machine translation (NMT) model, i.e., the Transformer. Our methods focus on training a single translation model for multiple domains by either learning domain specialized hidden state representations or predictor biases for each domain. We combine our methods with a previously proposed black-box method called mixed fine tuning, which is known to be highly effective for domain adaptation. In addition, we incorporate multilingualism into the domain adaptation framework. Experiments show that multilingual multi-domain adaptation can significantly improve both resource-poor in-domain and resource-rich out-of-domain translations, and the combination of our methods with mixed fine tuning achieves the best performance.