DeepAI AI Chat
Log In Sign Up

Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging

by   Emmanouil Stergiadis, et al.

Many modern Neural Machine Translation (NMT) systems are trained on nonhomogeneous datasets with several distinct dimensions of variation (e.g. domain, source, generation method, style, etc.). We describe and empirically evaluate multidimensional tagging (MDT), a simple yet effective method for passing sentence-level information to the model. Our human and BLEU evaluation results show that MDT can be applied to the problem of multi-domain adaptation and significantly reduce training costs without sacrificing the translation quality on any of the constituent domains.


page 1

page 2

page 3

page 4


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

Domain adaptation is a key feature in Machine Translation. It generally ...

Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey

The development of deep learning techniques has allowed Neural Machine T...

Multilingual Multi-Domain Adaptation Approaches for Neural Machine Translation

In this paper, we propose two novel methods for domain adaptation for th...

Compact Personalized Models for Neural Machine Translation

We propose and compare methods for gradient-based domain adaptation of s...

Building a Multi-domain Neural Machine Translation Model using Knowledge Distillation

Lack of specialized data makes building a multi-domain neural machine tr...

Translation Transformers Rediscover Inherent Data Domains

Many works proposed methods to improve the performance of Neural Machine...

Sentence Alignment with Parallel Documents Helps Biomedical Machine Translation

The existing neural machine translation system has achieved near human-l...