Work

Towards Robust Neural Machine Translation

Year: 2018

Type: conference-paper

Abstract: Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation (NMT) models.In this paper, we propose to improve the robustness of NMT models with adversarial stability training.The basic idea is to make both the encoder and decoder in NMT models robust against input perturbations by enabling them to behave similarly for the original input and its perturbed counterpart.Experimental results on Chinese-English, English-German and English-French translation tasks show that our approaches can not only achieve significant improvements over strong NMT systems but also improve the robustness of NMT models.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p18-1163

Authors: Yong Cheng, Zhaopeng Tu, Fandong Meng, Junjie Zhai, Yang Liu

Institutions: Tencent (China), State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Beijing Advanced Innovation Center for Language Resources, Tsinghua University

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Research Foundation, National Natural Science Foundation of China

Awards: G533029806, G6040666115, G8810487621