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Research on Semantic-based Passive Transformation in Chinese-English Machine Translation

Research on Semantic-based Passive Transformation in Chinese-English Machine Translation. Wenfei Chang, Zhiying Liu, Yaohong Jin Institute of Chinese Information Processing Beijing Normal University. Semantic analysis of passive voice. 2. 1. 3. 4. 5. Transformation rules and algorithm.

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Research on Semantic-based Passive Transformation in Chinese-English Machine Translation

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  1. Research on Semantic-based Passive Transformation in Chinese-English Machine Translation Wenfei Chang, Zhiying Liu, Yaohong Jin Institute of Chinese Information Processing Beijing Normal University

  2. Semantic analysis of passive voice 2 1 3 4 5 Transformation rules and algorithm Experiments and Result Analysis Introduction Conclusions Outline

  3. 1. Introduction

  4. 2. Semantic analysis of passive voice We have investigated 1000 sentences which should be transformed into English when translating. Table 1. Classification of Passive Sentence

  5. 2. Semantic analysis of passive voice • Sentences with passive mark in Chinese • 因此提交订单的交易者将被通知成交。(Thereby the trader that sent in the order will be informed about the deal.) • 它不需要处理在第一排列单元所接收的订单。(It does not need to handle the order that was received at the first ranking unit.) Passive mark BEI Passive mark SUO

  6. passive voice will be used in English ALL_PASS Verb+ Prep 2. Semantic analysis of passive voice • Sentences without passive mark in Chinese

  7. 2. Semantic analysis of passive voice • For example, • 1、 “V+NP” • 经由所述双向隧道转发分组。(Packets are forwarded via the bi-directional tunnel.) • 2、 “NP+V” • 固定的和旋转的磁鼓面对面地安装。(The fixed and rotatable drums are installed face to face.) • 包套可滑动地安装在可弯曲管内。(A sheath is slideably mounted inside the flexible pipe .) • 这种组合物可以做成很薄、很小的产品。（The composition can be made into a very thin and small product.） Component ellipsis in sentence. “Verb+Prep” structure in sentence. Effect Sentence

  8. 3. Transformation rules and algorithm A series of rules are drawn up according to several situations .The specific steps are as fellows:

  9. 4. Experiments and Result Analysis Table 2. Types of data Table 3. Result of transformation

  10. 4. Experiments and Result Analysis By analyzing errors in the result, we find there are mainly have three reasons: • Rules have not covered all kinds of linguistic phenomenon. • Knowledge base gives wrong property (“ALL_PASS[Y]”) to the verb. • The verb is wrongly recognized, thus leading to wrongly match the transformation rules.

  11. 5. Conclusions • Results show that our system has achieved a good effect. • In the future, we will make further improvements based on the errors.

  12. Thank you ! 2020/1/8

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