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Complete Multi-lingual Neural Machine Translation (C-MNMT) achieves superiorperformance against the conventional MNMT by constructing multi-way alignedcorpus, i.e., aligning bilingual training examples from different languagepairs when either their source or target sides are identical. However, sinceexactly identical sentences from different language pairs are scarce, the powerof the multi-way aligned corpus is limited by its scale. To handle thisproblem, this paper proposes "Extract and Generate" (EAG), a two-step approachto construct large-scale and high-quality multi-way aligned corpus frombilingual data. Specifically, we first extract candidate aligned examples bypairing the bilingual examples from different language pairs with highlysimilar source or target sentences; and then generate the final alignedexamples from the candidates with a well-trained generation model. With thistwo-step pipeline, EAG can construct a large-scale and multi-way aligned corpuswhose diversity is almost identical to the original bilingual corpus.Experiments on two publicly available datasets i.e., WMT-5 and OPUS-100, showthat the proposed method achieves significant improvements over strongbaselines, with +1.1 and +1.4 BLEU points improvements on the two datasetsrespectively.

EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation