@inproceedings{cd6595e5bac64f95b0ac3df1dd96e148,
title = "Pivot-based semantic splicing for neural machine translation",
abstract = "Current neural machine translation (NMT) usually extracts a fixedlength semantic representation for source sentence, and then depends on this representation to generate corresponding target translation. In this paper, we proposed a pivot-based semantic splicing model (PBSSM) to obtain a semantic representation including more translation information for source sentence, thus improving the translation performance of NMT. The spliced semantic representation is derived from source languages of trilingual parallel corpus by the pivot-based NMT. Besides, the proposed PBSSM only depends on one source language to generate its semantic representation during the encoding process. We integrated it into the NMT architecture. Experiments on the English-Japanese translation task show that our model achieves a substantial improvement by up to 22.9\% (3.74 BLEU) over the baseline.",
keywords = "Neural machine translation, Pivot-based translation, Semantic splicing",
author = "Di Liu and Conghui Zhu and Tiejun Zhao and Xiaoxue Wang and Muyun Yang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2016.; 12th China Workshop on Machine Translation, CWMT 2016 ; Conference date: 25-08-2016 Through 26-08-2016",
year = "2016",
doi = "10.1007/978-981-10-3635-4\_2",
language = "英语",
isbn = "9789811036347",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "14--24",
editor = "Shujie Liu and Muyun Yang",
booktitle = "Machine Translation - 12th China Workshop, CWMT 2016, Revised Selected Papers",
address = "德国",
}