no code implementations • Xintong Li, Lemao Liu, Guanlin Li, Max Meng, Shuming Shi
We find that although NMT models are difficult to capture word alignment for CFT words but these words do not sacrifice translation quality significantly, which provides an explanation why NMT is more successful for translation yet worse for word alignment compared to statistical machine translation.
no code implementations • ACL 2020 • Xintong Li, Lemao Liu, Rui Wang, Guoping Huang, Max Meng
This paper first provides a method to identify source and target contexts and then introduce a gate mechanism to control the source and target contributions in Transformer.
no code implementations • ACL 2019 • Xintong Li, Guanlin Li, Lemao Liu, Max Meng, Shuming Shi
Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, this paper finds attention may almost fail to capture word alignment for some NMT models.
no code implementations • NAACL 2018 • Xintong Li, Lemao Liu, Zhaopeng Tu, Shuming Shi, Max Meng
In neural machine translation, an attention model is used to identify the aligned source words for a target word (target foresight word) in order to select translation context, but it does not make use of any information of this target foresight word at all.