This paper describes Tencent AI Lab - Shanghai Jiao Tong University
(TAL-SJTU) Low-Resource Translation systems for the WMT22 shared task. We
participate in the general translation task on
English$\Leftrightarrow$Livonian. Our system is based on M2M100 with novel
techniques that adapt it to the target language pair. (1) Cross-model word
embedding alignment: inspired by cross-lingual word embedding alignment, we
successfully transfer a pre-trained word embedding to M2M100, enabling it to
support Livonian. (2) Gradual adaptation strategy: we exploit Estonian and
Latvian as auxiliary languages for many-to-many translation training and then
adapt to English-Livonian. (3) Data augmentation: to enlarge the parallel data
for English-Livonian, we construct pseudo-parallel data with Estonian and
Latvian as pivot languages. (4) Fine-tuning: to make the most of all available
data, we fine-tune the model with the validation set and online
back-translation, further boosting the performance. In model evaluation: (1) We
find that previous work underestimated the translation performance of Livonian
due to inconsistent Unicode normalization, which may cause a discrepancy of up
to 14.9 BLEU score. (2) In addition to the standard validation set, we also
employ round-trip BLEU to evaluate the models, which we find more appropriate
for this task. Finally, our unconstrained system achieves BLEU scores of 17.0
and 30.4 for English to/from Livonian.

本文介绍腾讯 AI 实验室和上海交通大学共同参加的一场机器翻译比赛，在使用 M2M100 进行翻译的过程中，采用了交叉模型词嵌入对齐、逐步自适应策略、数据增强和微调等技术手段取得了优异成绩。