Titlebook: Machine Translation; 15th China Conferenc Shujian Huang,Kevin Knight Conference proceedings 2019 Springer Nature Singapore Pte Ltd. 2019 ar

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Jiaxin Zhai,Zhengtao Yu,Shengxiang Gao,Zhenhan Wang,Liuqing Puto develop some of the content through their own examples anThe Subject A little explanation is in order for our choice of the title Linear Opti- 1 mization (and corresponding terminology) for what has traditionally been called Linear Programming.Theword programming in this context can be confusing

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Yulin Zhang,Chong Feng,Hongzheng Lito develop some of the content through their own examples anThe Subject A little explanation is in order for our choice of the title Linear Opti- 1 mization (and corresponding terminology) for what has traditionally been called Linear Programming.Theword programming in this context can be confusing

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Kehai Chen,Rui Wang,Masao Utiyama,Eiichiro Sumitanally been called Linear Programming.Theword programming in this context can be confusing and/or misleading to students. Linear programming problems are referred to as optimization problems but the general term linear p- gramming remains. This can cause people unfamiliar with the subject to think th

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Improving Bilingual Lexicon Induction on Distant Language Pairs,y, current solutions perform terribly on distant language pairs. To address this problem, we analyze existing models for the lexicon induction task of distant language pairs, such as English-Chinese. We propose an framework for the task with improved preprocessing, mapping and inference accordingly.

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Improving Quality Estimation of Machine Translation by Using Pre-trained Language Representation,ll suffers heavily from the problem that the quality annotation data remain expensive and small. In this paper, we focus on overcoming the limitation of QE data and explore to utilize the high level latent features learned by the pre-trained language models to reduce the model’s dependence on QE dat

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Incorporating Syntactic Knowledge in Neural Quality Estimation for Machine Translation, on neural networks have certain capability of implicitly learning the syntactic information from sentence-aligned parallel corpus. However, they still fail to capture the deep structural syntactic details of the sentences. This paper proposes a method that explicitly incorporates source syntax in n