Na Ye


Interactive-Predictive Machine Translation based on Syntactic Constraints of Prefix
Na Ye | Guiping Zhang | Dongfeng Cai
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Interactive-predictive machine translation (IPMT) is a translation mode which combines machine translation technology and human behaviours. In the IPMT system, the utilization of the prefix greatly affects the interaction efficiency. However, state-of-the-art methods filter translation hypotheses mainly according to their matching results with the prefix on character level, and the advantage of the prefix is not fully developed. Focusing on this problem, this paper mines the deep constraints of prefix on syntactic level to improve the performance of IPMT systems. Two syntactic subtree matching rules based on phrase structure grammar are proposed to filter the translation hypotheses more strictly. Experimental results on LDC Chinese-English corpora show that the proposed method outperforms state-of-the-art phrase-based IPMT system while keeping comparable decoding speed.


Productivity promotion strategies for collaborative translation on huge volume technical documents
Guiping Zhang | Na Ye | Fang Cai | Chuang Wu | Xiangkui Sun | Jinfu Yuan | Dongfeng Cai
Proceedings of Machine Translation Summit XV: User Track


Study on the Impact Factors of the Translators’ Post-editing Efficiency in a Collaborative Translation Environment
Na Ye | Guiping Zhang
Proceedings of Machine Translation Summit XIII: Papers


Using Multiple Discriminant Analysis Approach for Linear Text Segmentation
Jingbo Zhu | Na Ye | Xinzhi Chang | Wenliang Chen | Benjamin K Tsou
Second International Joint Conference on Natural Language Processing: Full Papers