Fusion of discourse structural position encoding for neural machine translation - Xiaomian KANG; Chengqing ZONG (智能科学与技术学报)
Most of existing document-level neural machine translation (DocNMT) methods focus on exploring the utilization of the lexical information of context,which ignore the structural relationships among the cross-sentence discourse semantic units.Therefore,multiple discourse structural position encoding strategies were proposed to represent the positional relationships among the words in discourse units over the discourse tree based on rhetorical structure theory (RST).Experimental results show that the source-side discourse structural position information is effectively fused into the DocNMT models underlying the Transformer architecture by the position encoding,and the translation quality is improved significantly.