Towards Better Chinese-centric Neural Machine Translation for Low-resource Languages

• URL: http://arxiv.org/abs/2204.04344v1
• Date: Sat, 9 Apr 2022 01:05:37 GMT
• Title: Towards Better Chinese-centric Neural Machine Translation for Low-resource Languages
• Authors: Bin Li, Yixuan Weng, Fei Xia, Hanjun Deng
• Abstract summary: Building a neural machine translation (NMT) system has become an urgent trend, especially in the low-resource setting. Recent work tends to study NMT systems for low-resource languages centered on English, while few works focus on low-resource NMT systems centered on other languages such as Chinese. We present the winner competition system that leverages monolingual word embeddings data enhancement, bilingual curriculum learning, and contrastive re-ranking.
• Score: 12.374365655284342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The last decade has witnessed enormous improvements in science and technology, stimulating the growing demand for economic and cultural exchanges in various countries. Building a neural machine translation (NMT) system has become an urgent trend, especially in the low-resource setting. However, recent work tends to study NMT systems for low-resource languages centered on English, while few works focus on low-resource NMT systems centered on other languages such as Chinese. To achieve this, the low-resource multilingual translation challenge of the 2021 iFLYTEK AI Developer Competition provides the Chinese-centric multilingual low-resource NMT tasks, where participants are required to build NMT systems based on the provided low-resource samples. In this paper, we present the winner competition system that leverages monolingual word embeddings data enhancement, bilingual curriculum learning, and contrastive re-ranking. In addition, a new Incomplete-Trust (In-trust) loss function is proposed to replace the traditional cross-entropy loss when training. The experimental results demonstrate that the implementation of these ideas leads better performance than other state-of-the-art methods. All the experimental codes are released at: https://github.com/WENGSYX/Low-resource-text-translation.

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