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

Introduction1

Semantic analysis of passive voice2

Transformation rules and algorithm3

Experiments and Result Analysis4

Conclusions5

Outline

1. Introduction

Type Sentence number Proportion

Sentences with passive mark 390 39%

Sentences without passive mark

610 61%

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

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.)

2. Semantic analysis of passive voice

Passive mark BEI

Passive mark SUO

ALL_PASS

passive voice will be used in English

Verb+ Prep

2. Semantic analysis of passive voice Sentences without passive mark in Chinese

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

2. Semantic analysis of passive voice

A series of rules are drawn up according to several situations . The specific steps are as fellows:

3. Transformation rules and algorithm

Type Total number

Should be transformed

Transformed

Right transformed

RB 1000 632 540 481

Google 1000 632 515 430

System Precision Recall

RB 89.1% 76.1%

Google 83.4% 68.1%

Table 2. Types of data

Table 3. Result of transformation

4. Experiments and Result Analysis

• 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.

4. Experiments and Result Analysis

By analyzing errors in the result, we find there are mainly have three reasons:

Results show that our system has achieved a good effect.

In the future, we will make further improvements based on the errors.

5. Conclusions

23/4/18

Thank you !Thank you !