Kyoto-U: NTCIR-7 Patent Translation System - Overview and Examples

1. Kyoto-U: Syntactical EBMT System for NTCIR-7 Patent Translation Task Kyoto University Toshiaki Nakazawa Sadao Kurohashi

2. Overview of Kyoto-U System Translation Examples J: 図書館で新聞を読む E: I read a newspaper in the library J: 政治の本が売れ残っている E: A book in politics was left on the shelf ・・・・・

3. Overview of Kyoto-U System Translation Examples I 図書館 で in library read 新聞 を newspaper ACC a newspaper 読む read in the library in politics a book 政治 の book NOM in politics 本 が was left left unsold 売れ残って いる on the shelf ・・・・・　　　　　　　　　　　・・・・・

4. Overview of Kyoto-U System Translation Examples Input: 図書館で政治の本を読む。 I 図書館 で read 新聞 を I a newspaper 読む 図書館 で read in the library in library 政治 の a book in politics 本 を in politics book ACC 読む in the library read a book 政治 の in politics 本 が was left 売れ残って いる Output: I read a book in politics in the library on the shelf ・・・・・　　　　　　　　　　　・・・・・

5. Alignment

6. J: 交差点で、突然あの車が 飛び出して来たのです。 E：The car came at me from the side at the intersection. Alignment

7. the car came at me 交差 from the side 点 で 、 at the intersection 突然 あの 車 が 飛び出して 来た のです Alignment • Transformation into dependency structure J: JUMAN/KNP E: Charniak’s nlparser → Dependency tree

8. the car came at me 交差 from the side 点 で 、 at the intersection 突然 あの 車 が 飛び出して 来た のです Alignment • Transformation into dependency structure • Detection of word(s) correspondences

9. Finding Correspondences • Bilingual dictionaries (500K entries) • Substring co-occurrence (Cromieres 2006) • Numeral normalization 二百十六万 → 2,160,000 ← 2.16 million • Transliteration (Katakana words, NEs) ローズワイン → rosuwain ⇔ rose wine (similarity:0.78) 新宿 → shinjuku ⇔ shinjuku (similarity:1.0)

10. the car came at me 交差 from the side 点 で 、 at the intersection 突然 あの 車 が 飛び出して 来た のです Alignment • Transformation into dependency structure • Detection of word(s) correspondences • Disambiguation of correspondences

11. the car came at me 交差 from the side 点 で 、 at the intersection 突然 あの 車 が 飛び出して 来た のです Alignment • Transformation into dependency structure • Detection of word(s) correspondences • Disambiguation of correspondences • Handling of remaining phrases Extension to leaf-nodes

12. the car came at me 交差 from the side 点 で 、 at the intersection 突然 あの 車 が 飛び出して 来た のです Alignment • Transformation into dependency structure • Detection of word(s) correspondences • Disambiguation of correspondences • Handling of remaining phrases • Registration to translation example database

13. 日本 で you 保険 will have to file 会社 に 対して insurance 保険 an claim 請求 の insurance 申し立て が with the office 可能です よ in Japan Alignment Ambiguities [in Japan] [insurance] [to the company] [insurance] [ofclaim] [file] [be able to]

14. Alignment: Consistency Near Far

15. For each pair of candidates ai and aj calculate the J-side distance dJ and the E-side distance dE • Give a consistency score to the pair based on dJ and dE • Calculate consistency scores for all the pairs in a possible set of alignment candidates

16. Baseline Distance of Each Branch: 1 Consistency Score: … … … 1/1+1/2=1.5

17. Consistency Score • The frequency of distance pair in gold-standard alignment data (Mainichi newspaper 40K sentence pairs) [Uchimoto04] Frequency (log) Dist of J-Side Dist of E-Side

18. ３ ３ デ格 NP 日本 で you １ 文節内 保険 will have to file ３ １ 連用 NN 会社 に 対して insurance ３ １ NP 文節内 保険 an claim １ ２ NN ノ格 請求 の insurance ３ ３ ガ格 PP 申し立て が with the office ３ PP 可能です よ in Japan Distance based on Dependency Type [in Japan] [insurance] [to the company] [insurance] [ofclaim] [file] [be able to]

19. ３ ３ デ格 NP 日本 で you １ 文節内 保険 will have to file ３ １ 連用 NN 会社 に 対して insurance ３ １ NP 文節内 保険 an claim １ ２ NN ノ格 請求 の insurance ３ ３ ガ格 PP 申し立て が with the office ３ PP 可能です よ in Japan Distance based on Dependency Type [in Japan] [insurance] [to the company] [insurance] [ofclaim] [file] [be able to]

20. デ格 NP 日本 で you 文節内 保険 will have to file 連用 NN 会社 に 対して insurance NP 文節内 保険 an claim NN ノ格 請求 の insurance ガ格 PP 申し立て が with the office PP 可能です よ in Japan Distance based on Dependency Type ３ ３ [in Japan] １ [insurance] ３ １ [to the company] ３ １ [insurance] １ ２ [ofclaim] ３ ３ [file] ３ [be able to]

21. Example of Alignment Improvement Proposed model Word-base alignment

22. Translation

23. Translation Translation Examples Input: 図書館で政治の本を読む。 I 図書館 で read 新聞 を I a newspaper 読む 図書館 で read in the library in library 政治 の a book in politics 本 を in politics book ACC 読む in the library read a book 政治 の in politics 本 が was left 売れ残って いる Output: I read a book in politics in the library on the shelf ・・・・・　　　　　　　　　　　・・・・・

24. Selection of Translation Examples • Score for an example • Size of an example • Similarity of neighboring nodes • Translation probability • Beam search from the root of the input [Sato 91]

25. I read a newspaper Translation example: in the library Input: I 図書館 で read 新聞 を 図書館 で in library a newspaper 政治 の 読む in politics in the library 本 を book ACC 読む 0.7 I read study a newspaper in the library

26. Combination of TMs Translation Examples Input: 図書館で政治の本を読む。 I 図書館 で read 新聞 を I a newspaper 読む 図書館 で read in the library in library 政治 の a book in politics 本 を in politics book ACC 読む in the library read a book 政治 の in politics 本 が was left 売れ残って いる on the shelf ・・・・・　　　　　　　　　　　・・・・・

27. Input：記録領域での変形形状と，記録特性の関係を調べた。Input：記録領域での変形形状と，記録特性の関係を調べた。 Output Dependency Tree Translation Examples Input Dependency Tree ┌ 状況 を 調べた 。 ┌the situation was examined ┌ the relationship ││　 ┌ deformation ││┌ shape and │││ │　┌ recording │││ └ in the region ││├ recording │└ between characteristics was examined ┌ 相互 　　┌作用 と 　　│┌記録 　　├特性 の ┌ 関係 を 調べた 。 　　　　　　┌ 記録 　　　　┌ 領域 で の 　　　　├ 変形 　　┌ 形状 と ， 　　│　┌ 記録 　　├ 特性 の ┌ 関係 を 調べた 。 ┌ the relationship ││┌interaction and ││├ recording │└ between characteristics was investigated 　 ┌cross-sectional ┌ shape ││　　┌ large ││┌deformation │└ in the region was └ simulated ┌大変 　　　　┌形 　　┌領域 で の 　　├断面 ┌形状 を 模擬 した Output： The relationship between deformation shape in the recording region and recording characteristics was examined . ┌ 記録 領域 の ┌ recording of the areas ┌ 変形 パターン を ┌ deformation the pattern

28. Evaluation ResultsandDiscussion

29. Intrinsic J-EEvaluation Result

30. Intrinsic E-JEvaluation Result

31. Critical Defect in EJ Translation • Not caring whether a child node is a pre-child or post-child • Resulting target structure goes wrong • After resolving this defect, BLEU score in EJ translation rose to 24.02 from 22.65 ? ? ? 24.02

32. Conclusion • Kyoto-U Fully Syntactic EBMT system: • Alignment: Consistency • Alignment: Extension • Translation: Discontinuous example • Translation: Easy combination • By using syntactic information, we could achieve reasonably high quality translation • For patent translation, we may need some pre-processings to handle special expressions which cause parsing errors