Chinese Word Segmentation Method for Domain-Special Machine Translation

Su Chen; Zhang Yujie; Guo Zhen; Xu Jin’an

Beijing Jiaotong University

Outline

Motivation

Method of combining multiple segmentation results

Experiment & Evaluation

Conclusion

Motivation 1/2

Training data

Test data F-measure

News domain

News domain

97.62%

Science 83.89%

●CTB test data●OOV ： 3.47%

●Science annotated data●OOV ： 22.4%

CTB training data

Motivation 2/2

Background: Development of a domain-specific Chinese-English machine translation system,

Problem: Accuracy of Chinese Word Segmentation (CWS) on large amounts of training text often decreases.

• Many errors in translation knowledge extraction

• Therefore seriously affects translation quality

Our resolution

Related work• Domain-Adapted Chinese Word Segmentation Based on

statistical Features

• In previous work, only 1-best result is adopted generally, and ignored the lower ranking result.

• Bilingually motivated domain-adapted word segmentation

• Many characters are aligned to NULL which decrease accuracy of Chinese segmentation.

Our goal ： Extend these method to augment domain adaptation of CWS

Our approach

We propose a linear model to combine multiple Chinese word segmentation results of the two segmenters to augment domain adaptation.

• Segmenter based on n-gram features of Chinese raw corpus.

• Segmenter based on bilingually motivated features.

Chinese raw corpus

Chinese raw corpus

Annotated corpus

Annotated corpus

ChinesesentencesChinese

sentences

EnglishSentences

EnglishSentences

Training CRF model

Training CRF model

CRF segmenterCRF segmenter

Word alignment result

Word alignment result

Bilingual segmenterBilingual

segmenter

ResultsResults

ResultResult

Linear-model forcombining

multipleresults

Linear-model forcombining

multipleresults

Segmentation result

Segmentation result

Framework

Annotated corpus

Annotated corpus

Extractingstatistical features

Extractingstatistical features

TrainingCRF model

TrainingCRF model

N-gram statisticalfeatures

N-gram statisticalfeatures

Raw corpusRaw corpus Test dataTest data

Extractingstatistical features

Extractingstatistical features

CRF Decoding

CRF Decoding

CRF segmenter Segmentation

resultSegmentation

result

CRF segmenter

Exploring statistical features of large-scale domain-specific Chinese raw corpus

• N-gram frequency feature

• N-gram AV (Accessor Variety) feature

Output of CRF models• N-best list of segmentation results

• Corresponding probability scores

Observation

Some erroneous segmentations in 1-best result are segmented correctly in the low-ranking results.

We intend to utilize correct parts within the 10-best results and the corresponding probability scores.

Bilingual segmenter

The boundaries of Chinese word are inferable on parallel corpus.

• Marked word boundaries in English sentences.

• Alignment from English word to Chinese word.

Inference step

1. Conduct word alignments using GIZA++, regarding each character of Chinese sentence as one word.

2. For each alignment ai=< ei, C>, if the characters in C

are consecutive in the sentence. Take C as a word

Calculate its confidence score (refer to paper)

Linear model

Calculate score of Cij being a word by combine

multiple segmentation results

K

kkkk jisegjiConfjiF

1

),(),(),(

F(i, j) denotes the score of characters from i to j being a

word.

λ (1≤k≤K) are weights of K segmentation results.

Confk(i,j) (1≤k≤K) is the confidence score of the kth segmentation

result.

segk(i, j) (1≤k≤K) is a two-valued function.

Decoding

Cij and F(i, j) being represented in a lattice

The best sequence is found by dynamic programming algorithm.

• Search a sequence of words with a maximum product of their scores.

Training parameter λ

Initial point λl (1≤l≤K): A point in K-dimensional

parameter space is randomly selected.

The parameters λl are optimized through iterative

process. • In each step, only one parameter is optimized, while

keeping all other parameters fixed.

Experiment setting

Experimental data: NTCIR-10 Chinese-English parallel patent description sentences

Annotation set: randomly selected 300 sentence pairs.

• 150 sentences used for training the lattice parameters.

• 150 sentences used for evaluation.

Evaluation

We conduct evaluations from two aspects:• Evaluation (1): accuracy of Chinese word segmentation

(F-measure)

• Evaluation (2): translation quality of MT system (BLEU)

Evaluation(1)

MethodPrecision

[%]Recall[%]

F-measure[%]

Bilingually motivated segmenter

73.1312 61.4480 66.7825

1-best of CRF segmenter(baseline)

90.2439 90.7710 90.5067

Linear-model(our approach)

91.6650 91.8614 91.7631

Accuracy of Chinese word segmentation

Evaluation(2)

We develop a phrase-based SMT with Moses, using different Chinese segmenters

• 1-best of CRF segmenter (baseline)

• Linear model (our approach)

• Stanford Chinese segmenter

• NLPIR Chinese segmenter

Evaluation (2): result

SMT using different Chinese segmenter BLEU[%]

1-best of CRF segmenter (baseline) 30.53

Linear model (our approach) 31.15

Stanford Chinese segmenter 30.98

NLPIR Chinese segmenter 30.56

Our approach increased by 0.62% compared to baseline. Performance of our approach is better than the two

popular segmenters.

Result Analysis

CRF 1-best result

Corresponding English word

Linear-model result

甘 || 氨酸 Glycine 甘氨酸聚合 || 物 Polymer 聚合物碳原子 Carbon || atoms 碳 || 原子碘复合物 Iodine || complex 碘 || 复合物

抗 || 微生物 Antimicrobial 抗微生物

Conclusion

We propose a linear model to combine multiple segmentation results from two segmenters to augment domain-adaptation.

• one based on n-gram statistical feature of large Chinese raw corpus.

• the other one based on bilingually motivated features of parallel corpus.

The experimental results show that both F-measure of CWS result and the BLEU score of SMT are improved.

Thanks!

Q&A