Complete Multi-lingual Neural Machine Translation (C-MNMT) achieves superior
performance against the conventional MNMT by constructing multi-way aligned
corpus, i.e., aligning bilingual training examples from different language
pairs when either their source or target sides are identical. However, since
exactly identical sentences from different language pairs are scarce, the power
of the multi-way aligned corpus is limited by its scale. To handle this
problem, this paper proposes "Extract and Generate" (EAG), a two-step approach
to construct large-scale and high-quality multi-way aligned corpus from
bilingual data. Specifically, we first extract candidate aligned examples by
pairing the bilingual examples from different language pairs with highly
similar source or target sentences; and then generate the final aligned
examples from the candidates with a well-trained generation model. With this
two-step pipeline, EAG can construct a large-scale and multi-way aligned corpus
whose diversity is almost identical to the original bilingual corpus.
Experiments on two publicly available datasets i.e., WMT-5 and OPUS-100, show
that the proposed method achieves significant improvements over strong
baselines, with +1.1 and +1.4 BLEU points improvements on the two datasets
respectively.

该论文提出了一种名为 “提取和生成” 的两步法，以构建大规模和高质量的多向对齐语料库，并在 WMT-5 和 OPUS-100 数据集上取得了很大进展。