In this paper, we describe our submission to the WMT19 low-resource parallel
corpus filtering shared task. Our main approach is based on the LASER toolkit
(Language-Agnostic SEntence Representations), which uses an encoder-decoder
architecture trained on a parallel corpus to obtain multilingual sentence
representations. We then use the representations directly to score and filter
the noisy parallel sentences without additionally training a scoring function.
We contrast our approach to other promising methods and show that LASER yields
strong results. Finally, we produce an ensemble of different scoring methods
and obtain additional gains. Our submission achieved the best overall
performance for both the Nepali-English and Sinhala-English 1M tasks by a
margin of 1.3 and 1.4 BLEU respectively, as compared to the second best
systems. Moreover, our experiments show that this technique is promising for
low and even no-resource scenarios.

本研究使用 LASER 工具包，基于编码 - 解码结构训练平行语料库，直接对多语言句子进行表征和过滤，通过与其他方法对比结果表明其在低资源及无资源情况下具有良好效果。