  In this paper, we describe our submissions to the WMT20 shared task on
parallel corpus filtering and alignment for low-resource conditions. The task
requires the participants to align potential parallel sentence pairs out of the
given document pairs, and score them so that low-quality pairs can be filtered.
Our system, Volctrans, is made of two modules, i.e., a mining module and a
scoring module. Based on the word alignment model, the mining module adopts an
iterative mining strategy to extract latent parallel sentences. In the scoring
module, an XLM-based scorer provides scores, followed by reranking mechanisms
and ensemble. Our submissions outperform the baseline by 3.x/2.x and 2.x/2.x
for km-en and ps-en on From Scratch/Fine-Tune conditions, which is the highest
among all submissions.
