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1 NTT 2012 NTT Corporation. All rights reserved.
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6 Noisy Channel f : (source), e : (target) ê = argmax e p(e f) = argmax e p(f e)p(e) 6
7 p( f e) (Brown+ 1990) f1 f2 f3 f4 f5 f6 f7 He is a high school student. a3=0 a2=4 φ e0 e1 e2 e3 e4 e5 p(fj ei) p(aj f, e) 7
8 p(aj f, e) IBM Model (Brown+ 1990) Model 1: Model 2: j, f, e Model 3: Model 2+ Model 4: Model 5: Model 4+ HMM Model (Vogel+ 1996): Model 1+ 8
9 (word alignment) IBM/HMM Models Viterbi GIZA++ MGIZA++ (GIZA++ multi-thread ) Chaski (Hadoop wrapper) Berkeley Aligner 9
10 (Koehn+ 2003) He is a high school student. He He high school student is a high school student p(f e) 10
11 Log-linear! ê = argmax e p(e f) = argmax e exp X k w k h k (f, e) p(f e), p(e f) p(e) 11
12 (Chiang 2007) S X1 is X2. a X He is a high school student. He high school student He high school student X He is a high school student. X1 S X2 X1 X2 X1 is a X2. 12
13 (Galley+ 2004, GHKM) S NP VP P NP NP He is a high school student. S NP VP P NP VP P VP is NP NP NP a NP NP NP high school student / / 13
14 ! ê = argmax e p(e f) = argmax e exp X k w k h k (f, e) Minimum Error Rate Training [Och 2003] Margin Infused Relaxed Alg. [Watanabe+ 2007] Pairwise Ranking Optimization [Hopkins+ 2011] 14
![2003] Margin Infused Relaxed Alg.](/docs-images/46/20550487/images/page_14.jpg "[Watanabe+ 2007] Pairwise Ranking Optimization")
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17 ê! ê = argmax e p(e f) = argmax e exp X k w k h k (f, e) O(n m n!) n m n 17
18 [Koehn 2003] He was His He is He He He is =1 =2 =3 =4 =5 18
19 [Chiang 2007, Zollmann 2006] S NP VP P NP VP P VP is NP NP NP a NP NP NP He NP high school student P. NP S He is a high school student. S VP NP NP NP VP P NP NP P 19
20 ... O(n!)... He lost his wallet in the airport yesterday. 20
21 Moses Philipp Koehn (U. Edinburgh) Footer 21
22 Moses 22
23 23
24 Pros: Cons:, Pros:,, Cons: = 24
25 BLEU IBM (Papineni+ 2002): de-facto standard n-gram We are delighted to inform you that your paper has been accepted. We are sorry to inform you that your paper was not accepted. 1-gram: 10/13 2-gram: 7/12 3-gram: 4/11 4-gram: 3/10 s Y BLEU = n p n min n n-gram 1, length(output) length(reference) brevity penalty : BLEU BLEU 25
26 WER (Word Error Rate) PER (Position-independent WER) TER (Translation Edit Rate) METEOR 26
27 RIBES NTT (Isozaki+ 2010, 2011) My paper was rejected because I drunk so much today. I drunk so much today because my paper was rejected. BLEU: 0.74 RIBES: 0.47 Kendall s τ : RIBES = 1+ kendall 2 p 1 BP brevity penalty * α=0.25, β=0.1 GPLv2 RIBES NTT Search 27
28 BLEU vs. RIBES : Spearman s ρ) ( ) BLEU RIBES NTCIR-9 PatentMT (Goto+ 2011) RIBES BLEU 28
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31 Further Reading... Philipp Koehn, Statistical Machine Translation Cambridge University Press, , 10, 2003 IBM (p.100 ) : ACL, NAACL, EACL, EMNLP, IJCNLP, AMTA, EAMT, MT Summit,... : Computational Linguistics, Machine Translation, ACM TALIP,... 31
32 ... ( ) 32
33 P. F. Brown et al., A Statistical Approach to Machine Translation, Computational Linguistics, vol. 16, no. 2 (1990) S. Vogel et al., HMM-Based Word Alignment in Statistical Translation, Proc. COLING (1996) 33
34 P. Koehn et al., Statistical Phrase-Based Translation, Proc. NAACL (2003) M. Galley et al., What s in a translation rule?, Proc. NAACL (2004) D. Chiang, Hierarchical Phrase-Based Translation, Computational Linguistics, vol. 33, no. 2 (2007) 34
35 F. J. Och, Minimum Error Rate Training in Statistical Machine Translation, Proc. ACL (2003) T. Watanabe et al., Online Large Margin Training for Statistical Machine Translation, Proc. EMNLP (2007) M. Hopkins and J. May, Tuning as Ranking, Proc. EMNLP (2011) 35
36 K. Papineni et al., BLEU: a Method for Automatic Evaluation of Machine Translation, Proc. ACL (2002) H. Isozaki et al., Automatic Evaluation of Translation Quality for Distant Language Pairs, Proc. EMNLP (2010) et al., RIBES:, (2011) I. Goto et al., Overview of the Patent Machine Translation Task at the NTCIR-9 Workshop, Proc. NTCIR-9 (2011) 36
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NAIST-IS-MT1251045 Factored Translation Models 2014 2 6 ( ) Kevin Duh Factored Translation Models Factored translation models Factored Translation Models, NAIST-IS-MT1251045, 2014 2 6. i Post-ordering
BLEU Kishore Papineni, Salim Roukos, Todd Ward and Wei-Jing Zhu. (2002) BLEU: a method for Automatic Evaluation of Machine Translation. ACL. MT ( ) MT
4. BLEU @NICT mutiyama@nict.go.jp 1 BLEU Kishore Papineni, Salim Roukos, Todd Ward and Wei-Jing Zhu. (2002) BLEU: a method for Automatic Evaluation of Machine Translation. ACL. MT ( ) MT ( ) BLEU 2 BLEU
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1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 1. [23] 1(a) 1(b) [19] n-best [1] 1 Nara Institute of Science and Technology a) akabe.koichi.zx8@is.naist.jp b) neubig@is.naist.jp c) ssakti@is.naist.jp
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taro.watanabe at nict.go.jp
taro.watanabe at nict.go.jp https://sites.google.com/site/alaginmt2014/ ... I want to study about machine translation. I need to master machine translation. machine translation want to study. infobox infobox
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統計的機械翻訳モデルの構築 各モデルを対訳文から学習 対訳文 太郎が花子を訪問した Taro visited Hanako. 花子にプレセントを渡した He gave Hanako a present.... モデル翻訳モデル並べ替えモデル言語モデル 2
ALAGIN 機械翻訳セミナー単語アライメント Graham Neubig 奈良先端科学技術大学院大学 (NAIST) 2014 年 3 月 5 日 https://sites.google.com/site/alaginmt2014/ 1 統計的機械翻訳モデルの構築 各モデルを対訳文から学習 対訳文 太郎が花子を訪問した Taro visited Hanako. 花子にプレセントを渡した He gave
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21 Pitman-Yor Pitman- Yor [7] n -gram W w n-gram G Pitman-Yor P Y (d, θ, G 0 ) (1) G P Y (d, θ, G 0 ) (1) Pitman-Yor d, θ, G 0 d 0 d 1 θ Pitman-Yor G
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tikeya[at]shoin.ac.jp The Function of Quotation Form -tte as Sentence-final Particle Tomoko IKEYA Kobe Shoin Women s University Institute of Linguisti
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tikeya[at]shoin.ac.jp The Function of Quotation Form -tte as Sentence-final Particle Tomoko IKEYA Kobe Shoin Women s University Institute of Linguistic Sciences Abstract 1. emphasis 2. Speaker s impressions
アジア言語を中心とした機械翻訳の評価 第 1 回アジア翻訳ワークショップ概要 Evaluation of Machine Translation Focusing on Asian Languages Overview of the 1st Workshop on Asian Translation
アジア言語を中心とした機械翻訳の評価 第 1 回アジア翻訳ワークショップ概要 Evaluation of Machine Translation Focusing on Asian Languages Overview of the 1st Workshop on Asian Translation 国立研究開発法人科学技術振興機構 PROFILE 情報企画部研究員 国立研究開発法人情報通信研究機構
4 23 4 Author s E-mail Address: kyamauchi@shoin.ac.jp; ksakui@shoin.ac.jp Japanese Elementary School Teachers Four Skills English Ability: A Self-evaluation Analysis YAMAUCHI Keiko, SAKUI Keiko Faculty
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Title yet についての 一 考 察 : yet, already, still, だ と もう Author(s) 吉 村, あき 子 Citation Osaka Literary Review. 28 P.16-P.29 Issue 1989-12-20 Date Text Version publisher URL http://hdl.handle.net/11094/25546 DOI
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中学生は機械翻訳された英語対話文完成問題を解けるか? 藤田彬 松崎拓也 登藤直弥 新井紀子 機械翻訳器の日常会話翻訳に対する性能を評価する試みについて紹介する. 前もって日本語に機械翻訳された英語の対話文完成問題を被験者が解き, その得点を用いて翻訳器の外的な評価を行った.300 名超の被験者を集めた大規模な調査により, 評価対象とした翻訳器のうち一つが 人間が文脈を考慮せずに行った翻訳 と同等の性能を有することが明らかになった.
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The Relationship of Word Power and Communicative Proficiency CHUJO, Kiyomi Nihon University TAKEFUTA, Junko Bunkyo Gakuin College TAKAHASHI, Hideo Chiba University TAKEFUTA, Yukio Bunkyo Gakuin University
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On Sapir's Principles of Historical Linguistics (I) An Interpretation on Sapir's View of Language Contact Nobuharu MIWA Abstract This paper is an atte
Title E. サピア 言語 (1921) の言語史原理 ( 前篇 ) : 語はどのように相互影響しあうか 解読 第 9 章言 Author(s) 三輪, 伸春 Citation 地域政策科学研究, 10: 171-194 Issue Date 2013-03-23 URL http://hdl.handle.net/10232/16669 http://ir.kagoshima-u.ac.jp
概要 単語の分散表現に基づく統計的機械翻訳の素性を提案 既存手法の FFNNLM に CNN と Gate を追加 dependency- to- string デコーダにおいて既存手法を上回る翻訳精度を達成
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