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Example-based Machine Translation based on Deeper NLP

Example-based Machine Translation based on Deeper NLP. Toshiaki Nakazawa 1 , Kun Yu 1 , Sadao Kurohashi 2. 1. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan, 113-8656 2. Graduate School of Informatics,

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Example-based Machine Translation based on Deeper NLP

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  1. Example-based Machine Translation based on Deeper NLP Toshiaki Nakazawa1, Kun Yu1, Sadao Kurohashi2 1. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan, 113-8656 2. Graduate School of Informatics, Kyoto University, Kyoto, Japan, 606-8501

  2. Outline • Why EBMT? • Description of Kyoto-U EBMT System • Japanese Particular Processing • Pronoun Estimation • Japanese Flexible Matching • Result and Discussion • Conclusion and Future Work

  3. Outline • Why EBMT? • Description of Kyoto-U EBMT System • Japanese Particular Processing • Pronoun Estimation • Japanese Flexible Matching • Result and Discussion • Conclusion and Future Work

  4. Why EBMT? • Pursuing deep NLP • Improvement of fundamental analyses leads to improvement of MT • Feedback from MT can be expected • EBMT setting is suitable in many cases • Not a large corpus, but similar translation examples in relatively close domain • e.g. manual translation, patent translation, …

  5. Outline • Why EBMT? • Description of Kyoto-U EBMT System • Japanese Particular Processing • Pronoun Estimation • Japanese Flexible Matching • Result and Discussion • Conclusion and Future Work

  6. came at me from the side Translation Examples at the intersection Input 交差 (cross) 交差点に入る時 私の信号は青でした。 点 で 、 (point) my 家 に to remove 突然 (suddenly) traffic 入る when 飛び出して 来た のです 。 The light (rush out) 交差 entering 時 was green (cross) a house 脱ぐ 点 に (house) when (point) 入る (enter) entering (enter) (when) 時 my 私 の (when) the intersection (put off) signature 私 の サイン (my) (my) 信号 は (signal) Language Model (signal) 青 信号 は traffic (blue) でした 。 The light 青 (signal) Output (was) でした 。 was green My traffic light was green when entering the intersection. (blue) (was) Kyoto-U System Overview

  7. Structure-based Alignment - Step1: Dependency structure transformation - Step2: Word/phrase correspondences detection - Step3: Correspondences disambiguation - Step4: Handling remaining words - Step5: Registration to database

  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 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. Step1 Dependency Structure Transformation • J: JUMAN/KNP • E: Charniak’s nlparser → Dependency tree

  9. Step2 Word Correspondence Detection • KENKYUSYA J-E, E-Jdictionaries (300K entries) • Transliteration (person/place names, Katakana words) Ex) 新宿 ⇔ shinjuku (similarity:1.0) → shinjuku sinjuku synjucu ... 交差 the car 点 で 、 came 突然 at me あの from the side 車 が at the intersection 飛び出して 来た のです

  10. Step3 Correspondence Disambiguation • Calculate correspondence score based on unambiguous alignment • Select correspondence with higher score distJ/E = Distance to unambiguous correspondence in Japanese/English tree

  11. 日本 で you 保険 will have 会社 に to file 対して insurance 保険 an claim 請求の insurance 申し立て が with the office in Japan 可能です よ Step3 Correspondence Disambiguation (cont.) 0.8 1.5 1.0

  12. Step4 Handling Remaining Words • Align root nodes when remained • Merge Base NP nodes • Merge into ancestor nodes 交差 the car 点 で 、 came 突然 at me あの from the side 車 が at the intersection 飛び出して 来た のです

  13. 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 Step5 Registration to Database • Register each correspondence • Register a couple of correspondences

  14. Translation • Translation example (TE) retrieval - for all the sub-trees in the input • TE selection - prefer to large size example • TE combination - greedily from the root node

  15. came at me from the side Translation Examples at the intersection 交差 (cross) 点 で 、 (point) my 家 に to remove 突然 (suddenly) traffic when 入る 飛び出して 来た のです 。 The light (rush out) 交差 entering 時 was green (cross) a house 脱ぐ 点 に (house) when (point) 入る (enter) entering (enter) (when) 時 my 私 の (when) the intersection (put off) signature 私 の サイン (my) (my) 信号 は (signal) (signal) 青 信号 は traffic (blue) でした 。 The light 青 (signal) (was) でした 。 was green (blue) (was) Combination Example Input

  16. came at me from the side Translation Examples at the intersection 交差 (cross) 点 で 、 (point) my 家 に to remove 突然 (suddenly) traffic when 入る 飛び出して 来た のです 。 The light (rush out) 交差 entering 時 was green (cross) a house 脱ぐ 点 に (house) when (point) 入る (enter) entering (enter) (when) 時 my 私 の (when) the intersection (put off) signature 私 の サイン (my) (my) 信号 は (signal) (signal) 青 信号 は traffic (blue) でした 。 The light 青 (signal) (was) でした 。 was green (blue) (was) Combination Example (cont.) Input

  17. Outline • Why EBMT? • Description of Kyoto-U EBMT System • Japanese Particular Processing • Pronoun Estimation • Japanese Flexible Matching • Result and Discussion • Conclusion and Future Work

  18. Pronoun Estimation • Pronouns are often omitted in Japanese sentences • Omitted in TE: - TE胃が痛いのです → I’ve a stomachache - Input私は胃が痛いのです → • Omitted in Input - TEこれを日本に送ってください →Will you mail this to Japan? - Input:日本へ送ってください → I I’ve a stomachache × Will you mail to Japan? × △

  19. Pronoun Estimation (cont.) • Estimate omitted pronoun by modality and subject case • Omitted in TE: - TE胃が痛いのです → I’ve a stomachache - Input私は胃が痛いのです → • Omitted in Input - TEこれを日本に送ってください →Will you mail this to Japan? - Input:日本へ送ってください → （私は）胃が痛いのです → I’ve a stomachache I’ve a stomachache○ （これを）日本へ送ってください → Will you mail this to Japan?○

  20. Various Expressions in Japanese • Synonymous Relation • Hiragana/Katakana/Kanji variations りんご ＝ リンゴ = 林檎 (apple) • Variations of Katakana expressions コンピュータ ＝ コンピューター (computer) • Synonymous words 登山 = 山登り(climbing mountain vs mountain climgbing) • Synonymous phrases 最寄りの = 一番近い Morphological Analyzer Automatically Acquired from Japanese Dictionaries (nearest) (most) (near) • Hypernym-Hyponym Relation • 災難 ← 災害 ← 地震(earthquake)、台風(typhoon) (disaster)

  21. Japanese Flexible Matching

  22. IWSLT06 Evaluation Results • Open data track (JE) • Correct recognition translation & ASR output translation

  23. Results Discussion • Punctuation insertion failure caused parsing error • Dictionary robustness affected alignment accuracy • TE selection criterion failed when choosing among ‘almost equal’ examples - e.g. Input: “買います” (buy a ticket) TE: “買いません” (not buy a ticket)

  24. Conclusion and Future Work • We not only aim at the development of MT, but also tackle this task from the viewpoint of structural NLP. • Implement statistical method on alignment • Improve parsing accuracies (both J and E) • Improve Japanese flexible matching method • J-C and C-J MT Project with NICT

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