Work

A Teacher-Student Framework for Zero-Resource Neural Machine Translation

Year: 2017

Type: conference-paper

Abstract: While end-to-end neural machine translation (NMT) has made remarkable progress recently, it still suffers from the data scarcity problem for low-resource language pairs and domains.In this paper, we propose a method for zero-resource NMT by assuming that parallel sentences have close probabilities of generating a sentence in a third language.Based on the assumption, our method is able to train a source-to-target NMT model ("student") without parallel corpora available guided by an existing pivot-to-target NMT model ("teacher") on a source-pivot parallel corpus.Experimental results show that the proposed method significantly improves over a baseline pivot-based model by +3.0 BLEU points across various language pairs.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/p17-1176

Authors: Yun Chen, Yang Liu, Yong Cheng, Victor O.K. Li

Institutions: University of Hong Kong, Tsinghua University, State Key Laboratory of Intelligent Technology and Systems

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Funders: National Natural Science Foundation of China, Tsinghua University, National High-tech Research and Development Program