f ê ê = arg max Pr(e f) (1) e M = arg max λ m h m (e, f) (2) e m=1 h m (e, f) λ m λ m BLEU [11] [12] PBMT 2 [13][14] 2.2 PBMT Hiero[9] Chiang PBMT [X
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1 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 1. Statistical Machine Translation: SMT[1] [2] [3][4][5][6] 2 Cascade Translation [3] Triangulation [7] Phrase-Based Machine Translation: PBMT[8] 1 Nara Institute of Science and Technology a) miura.akiba.lr9@is.naist.jp b) neubig@is.naist.jp c) ssakti@is.naist.jp d) tomoki@is.naist.jp e) s-nakamura@is.naist.jp PBMT Hierarchical Phrase-Based Machine Translation: Hiero[9] PBMT SMT Hiero PBMT Hiero [10] Koehn PBMT[8] PBMT PBMT 1
2 f ê ê = arg max Pr(e f) (1) e M = arg max λ m h m (e, f) (2) e m=1 h m (e, f) λ m λ m BLEU [11] [12] PBMT 2 [13][14] 2.2 PBMT Hiero[9] Chiang PBMT [X 1 ]visit[x 2 ] [X 1 ] [X 2 ] X 1 X 2 X 1 X 2 X 1,X 2 PBMT 3. PBMT 3.1 [3] 1 PBMT 2 1 n-best [4] 3.2 [3] 2 SMT De Gispert [3] 3.3 PBMT 3 Cohn [7] PBMT T FE,T EG 2
![FE T EG e T FE,T EG Utiyama [4] n =1n = 15 BLEU (6) 4. Hiero 4.1 3.1 PBMT 3.](/docs-images/76/74386625/images/3-1.jpg "3 Hiero PBMT Moses[15] Hiero Travatar[16] Moses PBMT Travatar Hiero Direct ( ) Cascade ( )")
3 1 2 T FG T FG φ( ) p ω ( ) φ ( f g ) ( ) = φ f e φ (e g) (3) φ ( g f ) ( ) = φ (g e) φ e f (4) ( ) ( ) p ω f g = f e pω (e g) (5) p ω ( ) ( ) p ω g f = p ω (g e) p ω e f f,e, g e T FE T EG e T FE,T EG Utiyama [4] n =1n = 15 BLEU (6) 4. Hiero PBMT 3.3 Hiero PBMT Moses[15] Hiero Travatar[16] Moses PBMT Travatar Hiero Direct ( ) Cascade ( ) Triangulation ( ) 3
![3 Direct SMT Cascade 3.1 PBMT Hiero 2 Triangulation 3.3 Moses PBMT (3)-(6) Moses [17] Travatar Hiero PBMT (3)-(6) f,e, g 4.2 MultiUN [10] 5 1 1 0.](/docs-images/76/74386625/images/4-0.jpg "5 300 Hiero 50 1500 1 Dataset Lang Words Sentencees Average Sentence Length En 13.2M 500k 26.3 Fr 15.7M 500k 31.3 Train Zh 12.4M 500k 24.8 Ar 11.6M 500k 23.2 Ru 11.9M 500k 23.9 En 37.9k 1.5k 25.")
4 3 Direct SMT Cascade 3.1 PBMT Hiero 2 Triangulation 3.3 Moses PBMT (3)-(6) Moses [17] Travatar Hiero PBMT (3)-(6) f,e, g 4.2 MultiUN [10] Hiero Dataset Lang Words Sentencees Average Sentence Length En 13.2M 500k 26.3 Fr 15.7M 500k 31.3 Train Zh 12.4M 500k 24.8 Ar 11.6M 500k 23.2 Ru 11.9M 500k 23.9 En 37.9k 1.5k 25.3 Fr 44.9k 1.5k 29.9 Dev Zh 35.0k 1.5k 23.4 Ar 33.2k 1.5k 22.2 Ru 34.5k 1.5k 23.0 En 38.5k 1.5k 25.7 Fr 45.2k 1.5k 30.2 Test Zh 36.0k 1.5k 24.0 Ar 33.6k 1.5k 22.2 Ru 34.7k 1.5k KyTea[18] 4
5 Moses PBMT Travatar Hiero KenLM[19] 5-gram GIZA++[20] Moses Travatar BLEU[11] BLEU Direct 2 Direct Triangulation Cascade 3 3 Direct Pivot Triangulation Cascade BLEU BLEU Score [%] Lang 1 Lang 2 Moses Hiero En Ar En Fr En Ru En Zh Ar Zh Fr Ru Fr Zh Ru Zh PBMT Triangulation Cascade 3 Hiero Triangulation Cascade Cascade Direct Cascade Direct 2 Hiero Triangulation Cascade PBMT 4.1 Hiero Triangulation PBMT (3)-(6) 1 X Hiero a X b() X c( ) X c( ) d X e( ) 2 a X b() c X d( ) X [21] 3 4 Hiero Triangulation Cascade 2 3 Hiero PBMT Hiero PBMT PBMT Hiero 5 Moses PBMT 7 PBMT 5. PBMT Hiero 5
6 Source Pivot Target MT Method BLEU Score [%] Direct Triangulation Cascade Ar En Zh Moses Hiero Fr En Zh Moses Hiero Ru En Zh Moses Hiero Zh En Ar Moses Hiero Zh En Fr Moses Hiero Zh En Ru Moses Hiero En Fr Zh Moses Hiero Zh Fr En Moses Hiero En Zh Fr Moses Hiero Fr Zh En Moses Hiero Hiero PBMT [22] Hiero [1] Peter F. Brown, Vincent J.Della Pietra, Stephen A. Della Pietra, and Robert L. Mercer. The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, Vol. 19, pp , [2] Christopher Dyer, Aaron Cordova, Alex Mont, and Jimmy Lin. Fast, easy, and cheap: construction of statistical machine translation models with mapreduce. In Proc. WMT, pp , [3] Adrià de Gispert and José B. Mariño. Catalan-english statistical machine translation without parallel corpus: Bridging through spanish. In Proc. of LREC 5th Workshop on Strategies for developing machine translation for minority languages, [4] Masao Utiyama and Hitoshi Isahara. A comparison of pivot methods for phrase-based statistical machine translation. In Proc. NAACL, pp , [5] Jörg Tiedemann. Character-based pivot translation for under-resourced languages and domains. In EACL12, pp , [6] Xiaoning Zhu, Zhongjun He, Hua Wu, Conghui Zhu, Haifeng Wang, and Tiejun Zhao. Improving pivotbased statistical machine translation by pivoting the cooccurrence count of phrase pairs. In Proc. EMNLP, [7] Trevor Cohn and Mirella Lapata. Machine translation by triangulation: Making effective use of multi-parallel corpora. In Proc. ACL, pp , June [8] Phillip Koehn, Franz Josef Och, and Daniel Marcu. Statistical phrase-based translation. In Proc. HLT, pp , [9] David Chiang. Hierarchical phrase-based translation. Computational Linguistics, Vol. 33, No. 2, pp , [10] Andreas Eisele and Yu Chen. MultiUN: A Multilingual Corpus from United Nation Documents. In Proc. of the Seventh conference on International Language Resources and Evaluation, pp , [11] Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. BLEU: a method for automatic evaluation of machine translation. In Proc. ACL, pp , [12] Franz Josef Och. Minimum error rate training in statistical machine translation. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1, pp , [13] Michel Galley and Christopher D. Manning. A simple and effective hierarchical phrase reordering model. In Proc. EMNLP, pp , [14] Isao Goto, Masao Utiyama, Eiichiro Sumita, Akihiro 6
7 Tamura, and Sadao Kurohashi. Distortion model considering rich context for statistical machine translation. In Proc. ACL, pp , August [15] Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, and Evan Herbst. Moses: Open source toolkit for statistical machine translation. In Proc. ACL, pp , [16] Graham Neubig. Travatar: A forest-to-string machine translation engine based on tree transducers. In Proc. ACL Demo Track, pp , [17] Philipp Koehn, Amittai Axelrod, Alexandra Birch Mayne, Chris Callison-Burch, Miles Osborne, and David Talbot. Edinburgh system description for the 2005 IWSLT speech translation evaluation. In Proc. IWSLT, [18] Graham Neubig, Yosuke Nakata, and Shinsuke Mori. Pointwise prediction for robust, adaptable Japanese morphological analysis. In Proc. ACL, pp , [19] Kenneth Heafield. KenLM: faster and smaller language model queries. In Proc, WMT, July [20] Franz Josef Och and Hermann Ney. A systematic comparison of various statistical alignment models. Computational Linguistics, Vol. 29, No. 1, pp , [21] Michel Galley, Mark Hopkins, Kevin Knight, and Daniel Marcu. What s in a translation rule? In Proc. HLT, pp , [22] Michael Paul, Hirofumi Yamamoto, Eiichiro Sumita, and Satoshi Nakamura. On the importance of pivot language selection for statistical machine translation. In Proc. NAACL, pp , June
Vol. 23 No. 5 December (Rule-Based Machine Translation; RBMT (Nirenburg 1989)) 2 (Statistical Machine Translation; SMT (Brown, Pietra, Piet
Graham Neubig, Sakriani Sakti 2 Improving Pivot Translation by Remembering the Pivot Akiva Miura, Graham Neubig,, Sakriani Sakti, Tomoki Toda and Satoshi Nakamura In statistical machine translation, the
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Graham Neubig, Sakriani Sakti 2,,,,, Improving Pivot Translation by Remembering the Pivot Akiva Miura, Graham Neubig,, Sakriani Sakti, Tomoki Toda and Satoshi Nakamura In statistical machine translation,
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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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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
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4-3 A Transliteration System Based on Bayesian Alignment and its Human Evaluation within a Machine Translation System Andrew Finch and YASUDA Keiji This paper reports on contributions in two areas. Firstly,
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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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RJ-008 Is Back Translation Really Unuseful? Validation of Back Translation from the Perspective of a Checking Method for Users Mai Miyabe Takashi Yoshino 1. [1, 2] [3] [4] 1 2 2 [3,5,6,7] [8, 9] 1: 2 3
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IPSJ SIG Technical Report Vol.2010-NL-199 No /11/ treebank ( ) KWIC /MeCab / Morphological and Dependency Structure Annotated Corp
1. 1 1 1 2 treebank ( ) KWIC /MeCab / Morphological and Dependency Structure Annotated Corpus Management Tool: ChaKi Yuji Matsumoto, 1 Masayuki Asahara, 1 Masakazu Iwatate 1 and Toshio Morita 2 This paper
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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1. 2 1 NEUBIG Graham 1 1 1 Improving Part-of-Speech Tagging by Combining Pointwise and Sequence-based Predictors Yosuke NAKATA, 1 Graham NEUBIG, 1 Shinsuke MORI 1 and Tatsuya KAWAHARA 1 This paper proposes
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1,a) 2, 1,b) 1,c) 3,d) 1,e) 2014 2 21, 2014 9 12 2 Automatic Generation of Shogi Commentary with a Log-linear Language Model Hirotaka Kameko 1,a) Makoto Miwa 2, 1,b) Yoshimasa Tsuruoka 1,c) Shinsuke Mori
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2 Sequence Alignment as a Set Partitioning Problem Masaaki Nishino,JunSuzuki, Shunji Umetani, Tsutomu Hirao and Masaaki Nagata Sequence alignment, which involves aligning elements of two given sequences,
フレーズベース機械翻訳システムの構築 フレーズベース機械翻訳システムの構築 Graham Neubig & Kevin Duh 奈良先端科学技術大学院大学 (NAIST) 5/10/2012 1
Graham Neubig & Kevin Duh 奈良先端科学技術大学院大学 (NAIST) 5/10/2012 1 フレーズベース統計的機械翻訳 ( SMT ) 文を翻訳可能な小さい塊に分けて 並べ替える Today I will give a lecture on machine translation. Today 今日は I will give を行います a lecture on の講義
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解 説 機械翻訳最新事情 : ( 上 ) 統計的機械翻訳入門 永田昌明渡辺太郎塚田元 NTT 科学基礎研究所 統計的機械翻訳 (statistical machin translation) は, 互いに翻訳になっている 2 つの言語の文の対から翻訳規則や対訳辞書を自動的に学習し, 言語翻訳を実現する技術である. この技術は過去 0 年間に大きく進歩し, アラビア語と英語のような語順が比較的近い言語対では,
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1 Example Based Dialogue System Based on Satisfaction Prediction Masahiro Mizukami Nara Institute of Science and Technology masahiro-mi@is.naist.jp Lasguido Nio lasguido.kp9@is.naist.jp Hideaki Kizuki
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4 97.52% tri-gram 92.76% 98.49% : Japanese word segmentation by Adaboost using the decision list as the weak learner Hiroyuki Shinnou In this paper, we propose the new method of Japanese word segmentation
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1 2 3 Incremental Linefeed Insertion into Lecture Transcription for Automatic Captioning Masaki Murata, 1 Tomohiro Ohno 2 and Shigeki Matsubara 3 The development of a captioning system that supports the
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Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura Scho
Haiku Generation Based on Motif Images Using Deep Learning 1 2 2 2 Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura 2 1 1 School of Engineering Hokkaido University 2 2 Graduate
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5-5 Fundamental Language Resources HASHIMOTO Chikara, Jong-Hoon Oh, SANO Motoki, and KAWADA Takuya Fundamental language resources are classifi ed into natural language processing tools and natural language
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DEIM Forum 2015 G8-5 Twitter 305 8550 1 2 305 8550 1 2 E-mail: s1111509@u.tsukuba.ac.jp, yohei@slis.tsukuba.ac.jp n-gram (n=3 9) 18 Twitter 1. 1. 1 Twitter 1 Twitter Twitter Twitter Twitter 1https://twitter.com
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1,a) 2 4 WC C WC C Grading Student programs for visualizing progress in classroom Naito Hiroshi 1,a) Saito Takashi 2 Abstract: To grade student programs in Computer-Aided Assessment system, we propose