@inproceedings{d25fb2cc1f974e7d8ebc0261c8c3adbc,
title = "Alignment-enhanced transformer for constraining NMT with pre-specified translations",
abstract = "We investigate the task of constraining NMT with pre-specified translations, which has practical significance for a number of research and industrial applications. Existing works impose pre-specified translations as lexical constraints during decoding, which are based on word alignments derived from target-to-source attention weights. However, multiple recent studies have found that word alignment derived from generic attention heads in the Transformer is unreliable. We address this problem by introducing a dedicated head in the multi-head Transformer architecture to capture external supervision signals. Results on five language pairs show that our method is highly effective in constraining NMT with pre-specified translations, consistently outperforming previous methods in translation quality.",
author = "Kai Song and Kun Wang and Heng Yu and Yue Zhang and Zhongqiang Huang and Weihua Luo and Xiangyu Duan and Min Zhang",
note = "Publisher Copyright: Copyright {\textcopyright} 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 34th AAAI Conference on Artificial Intelligence, AAAI 2020 ; Conference date: 07-02-2020 Through 12-02-2020",
year = "2020",
language = "英语",
series = "AAAI 2020 - 34th AAAI Conference on Artificial Intelligence",
publisher = "AAAI press",
pages = "8886--8893",
booktitle = "AAAI 2020 - 34th AAAI Conference on Artificial Intelligence",
}