Improving Context-Aware Neural Machine Translation with Source-side Monolingual Documents

Author:

Chen Linqing1,Li Junhui1,Gong Zhengxian1,Duan Xiangyu1,Chen Boxing2,Luo Weihua2,Zhang Min1,Zhou Guodong1

Affiliation:

1. School of Computer Science and Technology, Soochow University, Suzhou, China

2. Alibaba DAMO Academy

Abstract

Document context-aware machine translation remains challenging due to the lack of large-scale document parallel corpora. To make full use of source-side monolingual documents for context-aware NMT, we propose a Pre-training approach with Global Context (PGC). In particular, we first propose a novel self-supervised pre-training task, which contains two training objectives: (1) reconstructing the original sentence from a corrupted version; (2) generating a gap sentence from its left and right neighbouring sentences. Then we design a universal model for PGC which consists of a global context encoder, a sentence encoder and a decoder, with similar architecture to typical context-aware NMT models. We evaluate the effectiveness and generality of our pre-trained PGC model by adapting it to various downstream context-aware NMT models. Detailed experimentation on four different translation tasks demonstrates that our PGC approach significantly improves the translation performance of context-aware NMT. For example, based on the state-of-the-art SAN model, we achieve an averaged improvement of 1.85 BLEU scores and 1.59 Meteor scores on the four translation tasks.

Publisher

International Joint Conferences on Artificial Intelligence Organization

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. HanoiT: Enhancing Context-aware Translation via Selective Context;Database Systems for Advanced Applications;2023

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