论文标题
文档级神经机器翻译带有文档嵌入的
Document-level Neural Machine Translation with Document Embeddings
论文作者
论文摘要
标准神经机器翻译（NMT）是独立的文档级上下文的假设。大多数现有的文档级NMT方法对简短的文档级信息的淡淡感满足，而本工作则侧重于以多种形式的文档嵌入方式利用详细的文档级上下文，该文档嵌入式嵌入，该上下文能够充分建模更深，更丰富的文档级别的上下文。提出的文档感知的NMT可通过在源端引入全球和本地文档级线索来增强变压器基线。实验表明，所提出的方法显着改善了对强基础和其他相关研究的翻译性能。
Standard neural machine translation (NMT) is on the assumption of document-level context independent. Most existing document-level NMT methods are satisfied with a smattering sense of brief document-level information, while this work focuses on exploiting detailed document-level context in terms of multiple forms of document embeddings, which is capable of sufficiently modeling deeper and richer document-level context. The proposed document-aware NMT is implemented to enhance the Transformer baseline by introducing both global and local document-level clues on the source end. Experiments show that the proposed method significantly improves the translation performance over strong baselines and other related studies.
