The current status of Chinese- English EBMT -where are we now Joy (Ying Zhang) Ralf Brown, Robert Frederking, Erik Peterson Aug 2001

The current status of Chinese-English EBMT

-where are we now

Joy (Ying Zhang)

Ralf Brown, Robert Frederking, Erik Peterson

Aug 2001

Language Technologies InstituteSchool of Computer Science, Carnegie Mellon University

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Topics• Overview of this project

– Rapid deploy Machine Translation system between Chinese and English

• For HLT 2001 (Jun 00-Jan 01)– Augment the segmenter with new words found in the corpus

• For MT-Summit VIII Paper (Jan 01- May 01)– Two-threshold method used in tokenization code to find new words in corpus

• For PI meeting (Jun 01- Jul 01)– Accurate ablation experiments– Named-entities added to the training– Multi-corpora experiment

• After PI meeting (Aug 01)– Study of results reported for PI meeting– Review of evaluation methods– Type-token relations

• Plan for future research


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Overview of Ch-En EBMT• Adapting EBMT to Chinese

• Corpus used– Hong Kong legal code (from LDC)– Hong Kong news articles (from LDC)

• In this project:– Robert Frederking, Ralf Brown, Joy, Erik Peterson,

Stephan Vogel, Alon Lavie, Lori Levin,


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Corpus Cleaning• Convert from Big5 to GB• Divided into Training set (90%), Dev-test (5%)

and test set (5%)• Sentence level alignment, using Church & Gale

Method (by ISI)• Cleaned• Convert two-byte Chinese characters to their

cognates


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Corpus Statistics• Hong Kong Legal Code:

– Chinese: 23 MB– English: 37.8 MB

• Hong Kong News (After cleaning)– 7622 Documents– Dev-test: Size: 1,331,915 byte , 4,992 sentence pairs– Final-test: Size: 1,329,764 byte, 4,866 sentence pairs– Training: Size: 25,720,755 byte, 95,752 sentence pairs– Vocabulary size under LDC segmenter– Dev-test: Total type 8,529 Total token 134,749– Final-test: Total type 8,511 Total token 135,372– Training: Total type 20,451 Total token 2,600,095


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Chinese Segmentation• There are no spaces between Chinese words in written Chinese.

• The segmentation problem: Given a sentence with no spaces, break it into words


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Vague Definition of Words

• In English, word might be “a group of letters having meaning separated by spaces in the sentence”---- Doesn’t work for Chinese

• Is the word a single Chinese character?---Not necessarily

• Is the word the smallest set of characters that can have meaning by themselves? --- Maybe

• Is the word the longest set of characters that can have meaning by themselves? --- Perhaps


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Our Definition of Words/Phrases/Terms

• Chinese Characters– The smallest unit in written Chinese is a character, which is represented

by 2 bytes in GB-2312 code.

• Chinese Words– A word in natural language is the smallest reusable unit which can be used

in isolation.

• Chinese Phrases– We define a Chinese phrase as a sequence of Chinese words. For each

word in the phrase, the meaning of this word is the same as the meaning when the word appears by itself.

• Terms– A term is a meaningful constituent. It can be either a word or a phrase.


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Complicated Constructions

• There are some constructions that can cause problems for segmentation:– Transliterated foreign words and names: Using Chinese characters

for the sound of English names. The meaning of each character is irrelevant and can not be relied on. Each Chinese-speaking region will often transliterate the same name differently


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Complicated Constructions (2)

– Abbreviations: In Chinese abbreviations are formed by taking a character from each word in the phrase being abbreviated.

– Virtually any phrase can be abbreviated by taking on a character from each component, and these characters usually have no independent relation to each other


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– Chinese Names• Name = Surname (gen. one character) + Given

name (one or two characters)

• About 100 common surnames, but the number of given names is huge

• The complication for NLP: the same characters in names can be used in “regular” words. Just like in English: Bill Brown as a name.


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– Chinese Numbers• Similar to English, there are several ways to write

numbers in Chinese:


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Segmenter

• Approaches– Statistical approaches:

• Idea: Building collocation models for Chinese characters, such as first-order HMM. Place the space at the place where two characters rarely co-occur.

• Cons:– Data sparseness

– Cross boundary


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Segmenter (2)– Dictionary-based approaches

• Idea: Use a dictionary to find the words in the sentence

• Forward maximum match / backward maximum match/ or both direction

• Cons:– The size and quality of the dictionary used are of great

importance: New words, Named-entity

– Maximum (greedy) match may cause mis-segmentations


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Segmenter (3)

– A combination of dictionary and linguistic knowledge

• Ideas: Using morphology, POS, grammar and heuristics to aid disambiguation

• Pros: high accuracy (possible)

• Cons: – Require a dictionary with POS and word-frequency

– Computationally expensive


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Segmenter (4)

• We first used LDC’s segmenter

• Currently we are using a forward/backward maximum match segmenter for baseline. The word frequency dictionary is from LDC

• Word frequency dictionary from LDC: 43959 entries


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For HLT 2001

• Ying Zhang, Ralf D. Brown, and Robert E. Frederking. "Adapting an Example-Based Translation System to Chinese". To appear in Proceedings of Human Language Technology Conference 2001 (HLT-2001).


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For MT-Summit VIII

• Ying Zhang, Ralf D. Brown, Robert E. Frederking and Alon Lavie. "Pre-processing of Bilingual Corpora for Mandarin-English EBMT". Accepted in MT Summit VIII (Santiago de Compostela, Spain, Sep. 2001)

• Two-threshold for tokenization


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For MT-Summit VIII (2)


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For PI Meeting (1)

• Baseline System

• Full System

• Baseline + Named-Entity

• Multi-corpora System


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For PI Meeting (2)

• Baseline System


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For PI Meeting (3)

• Full System


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For PI Meeting (4)

• Named-Entity


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For PI Meeting (5)

• Multi-Corpora Experiment– Motivation– Corpus Clustering– Experiment


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Evaluation Issues

• Automatic Measures– EBMT Source Match– EBMT Source Coverage– EBMT Target Coverage– MEMT (EBMT+DICT) Unigram Coverage– MEMT (EBMT+DICT) PER

• Human Evaluations


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Evaluation Issues (2)

• Human Evaluations– 4-5 graders each time– 6 categories


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Evaluation Issues (3)


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After PI Meeting (0)• Study of results reported in PI meeting(http://pizza.is.cs.cmu.edu/research/internal/ebmt/tokenLen/index.htm)

– The quality of Named-Entity (Cleaned by Erik)

– Performance difference of EBMT while changing the average length of Chinese word token (by changing segmentation)

– How to evaluate the performance of the system

• Experiment of G-EBMT– Word clustering


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After PI Meeting (1)

• Changing the average length of Chinese token– No bracket on English– Use a subset of LDC’s frequency dictionary for

segmentation– Study the performance of EBMT system on

different average Chinese token length


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After PI Meeting (2)Thresh for freqFileEntries in Freq File#Type #Token type:token Avg. Len/typeAvg/token

INF 0 2325 238115 1:102.41 1.000 1.0001000 770 2686 208796 1:77.73 1.144 1.140800 933 2780 205043 1:73.75 1.175 1.160600 1188 2923 199837 1:68.36 1.220 1.190500 1347 3009 195630 1:65.01 1.246 1.217400 1619 3157 192331 1:60.92 1.288 1.238300 1978 3335 188984 1:56.66 1.333 1.260200 2640 3635 184878 1:50.86 1.403 1.288100 4062 4228 176129 1:41.66 1.515 1.35290 4298 4321 175302 1:40.57 1.530 1.35880 4606 4435 174039 1:39.24 1.547 1.36870 4964 4569 172729 1:37.80 1.569 1.37960 5381 4713 171357 1:36.36 1.589 1.39050 5940 4876 170290 1:34.92 1.612 1.39840 6726 5105 168526 1:33.01 1.642 1.41330 7928 5418 166273 1:30.69 1.681 1.432


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After PI Meeting (3)• Avg. Token Len. vs. StatDict Recall


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After PI Meeting (4)• Avg. Token Len. vs. Source word match


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After PI Meeting (5)• Avg. Token Len vs. Source Coverage


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After PI Meeting (6)• Avg. Token Len. Vs.


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After PI Meeting (7)• Avg. Token Len. Vs. Src/Tgt Coverage of EBMT in MEMT


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• Avg. Token Len. Vs. Translation Unigram Coverage


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After PI Meeting (9)• Avg. Token Len. Vs. Hypothesis Len (Len of translation)

The reference translation’s length is 1163 words


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• Avg. Token Len. Vs. PER


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After PI Meeting (11)• Type-Token curve for Chinese


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After PI Meeting (12)• Type-Token curve of Chinese and English


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Future Research Plan

• Generalized EBMT– Word-clustering– Grammar Induction

• Using Machine Learning to optimize the parameters used in MEMT

• Better Alignment Model: Integrating segmentation, brackting and alignment


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New Alignment Model (1)

• Using both monolingual and bilingual collocation information to segment and align corpus


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References

• Tom Emerson, “Segmentation of Chinese Text”. In #38 Volume 12 Issue2 of MultiLingual Computing & Technology published by MultiLingual Computing, Inc.