EAG：提取与生成多路对齐语料以实现完整多语言神经机器翻译
EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation
AI总结：
针对完整多语言神经机器翻译中多路对齐语料规模受限的问题，提出提取与生成两步法，通过配对相似句提取候选并用生成模型扩充，构建大规模高质量语料，在WMT-5和OPUS-100上分别提升1.1和1.4个BLEU点。
摘要：
完整多语言神经机器翻译（C-MNMT）通过构建多路对齐语料（即当不同语言对的源语或目标语相同时，对齐来自不同语言对的双语训练样本）相较于传统MNMT取得了更优的性能。然而，由于不同语言对中完全相同的句子较为稀缺，多路对齐语料的效果受限于其规模。为解决此问题，本文提出“提取与生成”（EAG）两步法，从双语数据中构建大规模、高质量的多路对齐语料。具体而言，我们首先通过配对具有高度相似源语或目标语句子的不同语言对的双语样本，提取候选对齐样本；然后利用训练良好的生成模型从候选中生成最终的对齐样本。通过这一两步流程，EAG能够构建大规模且多样性几乎与原始双语语料相同的多路对齐语料。在两个公开数据集（即WMT-5和OPUS-100）上的实验表明，所提方法相较于强基线取得了显著提升，在两个数据集上分别提升了+1.1和+1.4个BLEU点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
