  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.
