Structural Phrase Alignment Based on Consistency Criteria Toshiaki Nakazawa, Kun Yu, Sadao Kurohashi (Graduate School of Informatics, Kyoto University) {nakazawa, my traffic The light was green when entering the intersection Language Models My traffic light was green when entering the intersection. Output came at me from the side at the intersection 私 の私 の サイン 家 に家 に 入る 時 脱ぐ 交差 点 で 、点 で 、 突然 飛び出して 来た のです 。 信号 は 青 でした 。 my signature traffic The light was green to remove when entering a house Translation Examples (suddenly) (rush out) (house) (put off) (signal) (enter) (when) (cross) (point) (my) (signal) (blue) (was) Input 交差 点 に点 に 入る 時 私 の私 の 信号 は 青 でした 。 (cross) (point) (enter) (when) (my) (signal) (blue) (was) 交差点に入る時 私の信号は青でし た。 Near! Far! J-Side DistanceE-Side Distance Consistency Score Frequency (log) Dist of J-Side Dist of E-Side Score J-Side Distance E-Side Distance Flow of Our EBMT System Core Steps of Alignment Searching Correspondence Candidates –Fine alignment is efficient in translation –Search candidates as much as possible using variety of linguistic information Bilingual dictionaries Transliteration (Katakana words, NEs) ローズワイン → rosuwain ⇔ rose wine (similarity:0.78) 新宿 → shinjuku ⇔ shinjuku (similarity:1.0) Numeral normalization 二百十六万 → 2,160,000 ← 2.16 million Japanese flexible matching (Odani et. al. 2007) Substring co-occurrence measure (Cromieres 2006) Selecting Correspondence Candidates –More candidates derive more ambiguities and improper alignments –Necessity of robust alignment method which can align parallel sentences consistently by selecting the adequate candidates set PreRecF Baseline Consistency Score Proposed(+CS,+DpndType) Filtering (80%) Moses (SMT Toolkit)* Manual (upper bound) English- French English- Romanian English- Korean HLT-NAACL ACL （ Gildea, 2003 ） --32 GIZA Experimental Result 500 test sentences from Mainichi newspaper parallel corpus Bilingual dictionary: KENKYUSYA J-E/J-E 500K entries Evaluation criteria: Precision / Recall / F-measure Character-base for Japanese, word-base for English Quality of Other Language Pairs * Using 300K newspaper domain bi-sentences for training (AER) Conclusion Selecting Correspondence Candidates Using Consistency Score and Dependency Type you will have to file insurance an claim insurance with the office in Japan 日本 で 保険 会社 に 対して 保険 請求 の 申し立て が 可能ですよ (in Japan) (insurance) (to company) (claim) (instance) (you can) Ambiguities! Improper alignments! Distribution of the distance of alignment pairs in hand-annotated data (Mainichi newspaper 40K sentence pairs) [Uchimoto04] Consistency Score Function “Near-Near” pair → Positive Score “Far-Far” pair → ０ “Near-Far” pair → Negative Score 1/1+1/2=1.5 baseline Japanese predicate: level C6 predicate: level B+/B5 predicate: level B-/A4 case no / rentai2 Inside clause1 predicate: level A- Others3 English S / SBAR / SQ …5 VP / WHADVP4 WHADJP ADVP / ADJP NP / PP / INTJ 3 QP / PRT / PRN Others1 Dependency Type Distance How to reflect the inconsistency? Proposed a new phrase alignment method using consistency criteria. Enough alignment accuracy compared to other language pairs. We need to acquire the parameters automatically by machine learning. We are planning to evolve the framework which revises the parse result. (There is a translation demos in exhibition corner by NICT which is using our system!) you will have to file insurance an claim insurance with the office in Japan 日本 で 保険 会社 に 対して 保険 請求 の 申し立て が 可能です よ ３ １ １ ３ ２ ３ ３ ３ ３ １ １ デ格 文節内 連用 文節内 ノ格ノ格 ガ格 NP NN PP NN PP ３ Pair 1: (Ds, Dt) = (1, 1) Positive Score Pair 2: (Ds, Dt) = (1, 7) Negative Score (in Japan) (insurance) (to company) (claim) (instance) (you can) [case “de”] [case “ga”] [renyou] [case “no”] [inside clause] Near! Far!