1. Automatic Transliteration for Japanese-to-English Text Retrieval
Yan Qu, Gregory Grefenstette, David A. Evans
Clairvoyance Corporation
SIGIR 2003
Toronto, Canada

2. Cross Language Information Retrieval (CLIR)
User query in one Language, documents to be retrieved in another
Common Approach
Translate query into target language using bilingual translation dictionary
Then, monolingual search
Problem: unknown word
Word not in translation dictionary
Proper name, technical term
Solutions
Cognates, or pass through unchanged
languages with different writing systems — transliteration

3. English-to-Japanese Transliteration
Katakana – Japanese syllabic alphabet, e.g.,
コ, ン, ピ, ュ, ー, タ
Applies to foreign proper names and borrowed technical terms
“computer” – “konpiyuuta” – コンピュータ
コ for “ko”
ン for “n”
ピ for “pi”
ュー for “yuu”
タ for “ta”

4. Does CLIR performance improve?
Research Questions
How to automatically augment an English-to-Japanese translation dictionary with new transliterations?
Explore generate and attest method
Does CLIR performance improve?

5. Generate and Attest Method
Generate possible transliterations
English word to English sound sequence
English sounds to Japanese sounds
Japanese sounds to katakana
Attest validity with corpus statistics
Use monolingual Japanese corpus to attest katakana

6. Generate and Attest Method (Cont.)
Y:y
Y:I
Y:yu …
AH:a
AH:o
AH:e …
English pronunciation
dictionary
English-Japanese
phoneme mapping
English
Word
e.g., YEMEN
Get EN sound
sequence
Map to possible JP
sound sequences
YEMEM:
y-e-m-a-n
i-e-m-a-n
yu-e-m-a-n …
yu-a-n-u-n
Y-EH-M-AH-N
Get katakana
sequences
Discard
イエマン
ユエマン …
ユアヌン
Validate katakana sequences
イエマン
ユエマン -- 0 …
ユアヌン -- 0
Kana to JP phoneme
mapping
web
corpus

7. English Word to English Phone
CMUDICT .0.6.
Carnegie Mellon University Pronunciation Dictionary
39 English “phonemes”
Phoneme Example Translation
AA odd AA D
AE at AE T
AH hut HH AH T
AO ought AO T
AW cow K AW

8. Stress information is removed
English Word to English Phone (cont.)
CMUDICT Lexicon Sample
ACTOR'S AE1 K T ER0 Z
ACTORS AE1 K T ER0 Z
ACTORS' AE1 K T ER0 Z
ACTRESS AE1 K T R AH0 S
ACTRESS'S AE1 K T R AH0 S AH0 Z
ACTRESSES AE1 K T R AH0 S AH0 Z
ACTS AE1 K T S
ACTS(2) AE1 K S
ACTUAL AE1 K CH AH0 W AH0 L
ACTUAL(2) AE1 K SH AH0 L
ACTUALITY AE2 K CH AH0 W AE1 L AH0 T IY0
ACTUALIZE AE1 K CH AH0 W AH0 L AY2 Z
ACTUALLY AE1 K CH AH0 W AH0 L IY0
Stress information is removed

9. English Phone to Japanese Phone
Mapping between English and Japanese Phonemes (Knight & Graehl, 1998)
EnglishPhone JapanesePhone Prob
AA o
AA a
AA aa
AA oo
AE a
AE ya

10. English Phone to Japanese Phone (cont.)
Generate hypothetical Japanese sound sequences
Heuristics for pruning hypothesis space
Discard if the last sound is a consonant (except “n”)
Discard if EN-JP mapping probability falls under 0.05

11. English Phone to Japanese Phone (cont)
computer:
kampyuutaa
kuampyuutaa
kkuampyuutaa
kompyuutaa
kuompyuutaa
kkuompyuutaa
kempyuutaa
kuempyuutaa
kkuempyuutaa
kimpyuutaa …

12. Japanese Phone to Katakana
Mapping between katakana characters and phonemes
ボ bo
ポ po
マ ma
ミ mi
ム mu
メ me
モ mo
トヮ toa
モヮ moa
ウィ wi
ウェ we
ウォ wo
ファ fa
フィ fi

13. Japanese Phone to Katakana (cont.)
Longest match to segment JP sound sequences
konpyuutaa (computer)  ko n pyu u ta a
If vowel lengthening, add “ー”
supiido (speed)  スピード(su pi i do)
If consonant doubling (t, k, p, s, etc), add “ッ”
baggu (bag)  バッグ (ba g gu)
If multiple mappings, keep all candidates
If mapping unavailable, discard

14. Katakana Sequence Validation
Use monolingual Japanese corpus for validation
If the sequence is attested, then it’s validated
The frequency of the attested sequence is recorded
YEMEN イエメン
COMPUTER コンピュータ 8331
COMPUTER コンピューター 6184
COMPUTER コンピュタ
COMPUTER コンピュター
COMPUTER カンピューター
COMPUTER コムピュータ
COMPUTER コンピユーター

15. Prediction Accuracy English Name to Katakana top candidate 860 58.5
matches %
top candidate
860
58.5
top 2 candidates
1094
74.5
top 3 candidates
1158
78.8
top 4 candidates
1185
80.7
top 5 candidates
1202
81.8
top 6 candidates
1207
82.2
top 7 candidates
1211
82.4
Results based on 1,469 English names, evaluated against a gold standard

16. Japanese-to-English IR
Target documents
LA Times 94 from CLEF: 113,000 documents
Japanese source queries
CLEF 2001: 37 topics; CLEF 2002: 30 topics
Topics with title field and description field
With at least one katakana sequence
<num> C091 </num>
<JA-title>ラテンアメリカにおけるAI</JA-title>
<JA-desc> ラテンアメリカにおける人権についてのアムネスティ・インターナショナルの報告書</JA-desc>
<JA-narr> 適合文書は、ラテンアメリカにおける人権に関するアムネスティ･インターナショナルの報告書、またはこの報告書に対する反応についての情報を読者に提供するもの。</JA-narr>

17. Japanese-to-English IR (Cont.)
Japanese word segmentation
Dictionary-based
EDICT
EDICT expanded with transliterations
Japanese topic translation
Use same dictionaries mentioned above
Pick best translation candidate by measuring pair-wise coherence of translation alternatives (Qu, Grefenstette, Evans, CLEF 2002)
Validation Corpora — independent variable
Japanese Corpus from LDC (230 MB newswire)
Japanese Corpus from NTCIR-1 (240 MB technical abstracts)
LDC plus NTCIR1

18. Japanese-to-English IR Results
CLEF 2001 Topics
No feedback
Recall
Avg. Prec
No translit (baseline)
476 / 587
0.2667
Translit, NTCIR1
457 (-5.0%)
(+8.3%)
Translit, LDC
491 (+3.2%)
(+10.7%)
Translit, NTCIR1+LDC
With feedback
Recall
Avg. Prec
No translit (baseline)
508 / 587
0.2763
Translit, NTCIR1
488 (-3.9%)
(+2.5%)
Translit, LDC
517 (+1.8%)
(+9.7%)
Translit, NTCIR1+LDC

19. Japanese-to-English IR Results
CLEF 2002 Topics
No feedback
Recall
Avg. Prec
No translit (baseline)
396 / 513
0.1518
Translit, NTCIR1
391 (-1.3%)
(+45.3%)
Translit, LDC
407 (+2.8%)
(+64.8%)
Translit, NTCIR1+LDC
(+64.8%)
With feedback
Recall
Avg. Prec
No translit (baseline)
367 / 513
0.2063
Translit, NTCIR1
390 (+6.3%)
(+33.1%)
Translit, LDC
405 (+10.4%)
(+31.2%)
Translit, NTCIR1+LDC

20. CLEF 2002 Topic-by-Topic Differences

21. Reasons for Better Performance
Improved segmentation and translation
E.g., Topic 85 (“Turquoise Program in Rwanda”)
In EDICT
No entry for ルワンダ as Rwanda
Automatic transliteration adds:
ルワンダ: Rwanda
Increased precision from to
Added new translations
E.g., Topic 47 (“Russian Intervention in Chechnya”)
チェチェン: Chechin; Chechnia
Automatic transliteration adds
チェチェン: Chechen
Increased precision from to

22. Error Analysis Transliteration not from English Incomplete mapping
ベネチア from Italian Venezia, not Venice
Incomplete mapping
Query term Energy エネルギー (e-ne-ru-gi-i) not in the hypothesis space
Erroneous mapping from Japanese sound sequences to katakana
Greedy match algorithm used
Insufficient coverage of the reference corpus
Chechen not present in NTCIR1 corpus

23. Related Work
English/Japanese (Knight & Graehl 98), English/Korean (Kang & Choi 99) , English/Chinese (Meng, et al., 01), English/Arabic (Stalls & Knight 98)
Knight & Graehl (98)
Focused on back-transliteration
Fujii & Ishikawa (01)
English-string-to-Japanese-string transliteration with corpus validation, also improved CLIR

24. Conclusion
Automated English-to-Japanese transliteration (for names & technical terms) is possible through generation and validation
Augmenting translation dictionary with discovered transliteration improves CLIR performance

25. The End