(Statistical Machine Translation: SMT [1])
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1 1,a) Graham Neubig 1,b) Michael Paul 2,c) 1,d) n-gram 1. (Statistical Machine Translation: SMT [1]) (Active Learning) [2][3][4][5][6][7] [2] 1 Nara Institute of Science and Technology 2 ATR-Trek a) miura.akiba.lr9@is.naist.jp b) neubig@is.naist.jp c) michael.paul@atr-trek.co.jp d) s-nakamura@is.naist.jp (a) n-gram (n =4) (b) (c) 1 n-gram [5] 2 1 (a) c 2015 Information Processing Society of Japan 1
2 ( ) n =4 any one of the () 1(b) ( ) [8] 1(c) () 2. Algorithm 1 1: Init: 2: SrcPool 3: Translated 4: Oracle 5: Loop Until : 6: TM TrainTranslationModel(Translated) 7: NewSrc SelectNextPhrase(SrcPool,Translated,TM) 8: NewTrg GetT ranslation(oracle, NewSrc) 9: Translated Translated { NewSrc,NewTrg } 1 4 SrcPool Translated Translated Oracle Translated 7 SrcPool, Translated, TM 3. n-gram n-gram 3.1 n-gram n-gram n n-gram n-gram Bloodgood n =4 n-gram 80% BLEU [9] [5] n-gram n-gram 3.1 n c 2015 Information Processing Society of Japan 2
3 Bloodgood n-gram BLEU [5] 1 n =4 n = n-gram Okanohara [8] s 1 s 2 α, β : s 1 = αs 2 β occ(s 1 )=occ(s 2 ) (1) s 1,s 2,α,β 0 occ( ) p 1 = one of the preceding, occ(p 1) = 200, 000 p 2 = one of the preceding claims, occ(p 2) = 200, 000 p 3 = any one of the preceding claims, occ(p 3) = 190, 000 p 1 = αp 2 β α = β = claims p 1 p 2 p 2 p 3 p 1 p 2 occ(p 1 )=occ(p 2 ) = 200, p 1 p 2 p 2 p 3 occ(p 2 ) = 200, , 000 = occ(p 3 ) p 2 p 3 1 s 1 s 1 s 2 s 2 s 1 s 1 p 1 p 2 p 1 p 2 p p p 2 p 2 [10] 2 p 1 p 2 p 1 p 2 p 3 p 2 p 3 p 2 p 3 1 s 1 s2 α, β : s 1 = αs 2 β occ(s 1) 2 <occ(s 2 ) (2) n-gram c 2015 Information Processing Society of Japan 3
4 2 (a) are proposed (b) are proposed two methods are proposed are proposed 2(b) are proposed 2 n-gram / En-Ja En-Fr 1 414k () Train En: 6.72M ( ) Train Test Dev Ja: 9.69M 1.87M En: 46.4M Ja: 57.6M M () Train En: 47.6M ( ) Train Test Dev Fr: 49.4M 15.5M En: 393M Fr: 418M ( 3 ) ASPEC *1 WMT2014 *2 Europarl EMEA PatTR Wikipedia KyTea 60 1 [11] GIZA++ inc-giza-pp *3 Moses MMSAPT(Memory-mapped Dynamic Suffix Array Phrase Tables) KenLM [12] n =5 n-gram MERT [13] BLEU [9] 8 *1 *2 *3 c 2015 Information Processing Society of Japan 4
5 3 BLEU ( : 10 : 100: 10: 100 ) (sent-rand): (4gram-rand): 4 4-gram (sent-by-4gram-freq): 4 (3.1 ) 4-gram (4gram-freq): 4 (3.2 ) (maxsubst-freq): (4.1 ) (struct-freq): (4.2 ) (reduced-maxsubst-freq): (4.1 ) (reduced-struct-freq): (4.1, 4.2 ) 1 Ckylark [14] gram-freq c 2015 Information Processing Society of Japan 5
6 1 sent-by-4gram-freq 1.28M 33.6M gram-freq 8.48M 26.0M k 2.13 En-Ja maxsubst-freq 7.29M 25.8M k 2.22 reduced-maxsubst-freq 6.06M 21.7M k 2.10 struct-freq 1.45M 4.85M k 1.51 reduced-struct-freq 1.10M 3.33M k 1.49 sent-by-4gram-freq 10.6M 269M gram-freq 40.1M 134M k 2.76 En-Fr maxsubst-freq 62.4M 331M k 4.17 reduced-maxsubst-freq 45.9M 246M k 3.39 struct-freq 14.1M 94.2M k 2.49 reduced-struct-freq 7.33M 41.3M k ( 3 ) 1-gram / 4-gram [%] sent-rand / / / gram-rand / / / 5.98 sent-by-4gram-freq / / / En-Ja 4gram-freq / / / / maxsubst-freq / / / reduced-maxsubst-freq / / /10.57 struct-freq / / / 7.02 reduced-struct-freq / / / 7.14 sent-rand / / / gram-rand / / / sent-by-4gram-freq / / / En-Fr 4gram-freq / / / / maxsubst-freq / / / reduced-maxsubst-freq / / / struct-freq / / / reduced-struct-freq / / / maxsubst-freq struct-freq reduced-struct-freq 4gram-freq maxsubst-freq reduced-maxsubst-freq struct-freq reducedstruct-freq 1 2 c 2015 Information Processing Society of Japan 6
7 n-gram 4-gram gram 4-gram 3 reduced-struct-freq 1-gram 4-gram 3 sent-by-4gram-freq 1 4-gram 4 1-gram 4-gram 6. 4-gram ( )ATR-Trek [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] Matthias Eck, Stephan Vogel, and Alex Waibel. Low Cost Portability for Statistical Machine Translation based in N-gram Frequency and TF-IDF. In Proc. IWSLT, pp , [3] Gholamreza Haffari and Anoop Sarkar. Active Learning for Multilingual Statistical Machine Translation. In Proc. ACL, pp , August [4] Sankaranarayanan Ananthakrishnan, Rohit Prasad, David Stallard, and Prem Natarajan. A Semi-Supervised Batch-Mode Active Learning Strategy for Improved Statistical Machine Translation. In Proc. CoNLL, pp , July [5] Michael Bloodgood and Chris Callison-Burch. Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation. In Proc. ACL, pp , July [6] Jesús González-Rubio, Daniel Ortiz-Martínez, and Francisco Casacuberta. Active learning for interactive machine translation. In Proc. EACL, pp , April [7] Spence Green, Sida I. Wang, Jason Chuang, Jeffrey Heer, Sebastian Schuster, and Christopher D. Manning. Human Effort and Machine Learnability in Computer Aided Translation. In Proc. EMNLP, pp , October [8] Daisuke Okanohara and Jun ichi Tsujii. Text Categorization with All Substring Features. In Proc. SDM, pp , [9] Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. Bleu: a Method for Automatic Evaluation of Machine Translation. In Proc. ACL, pp , July [10] Toru Kasai, Gunho Lee, Hiroki Arimura, Setsuo Arikawa, and Kunsoo Park. Linear-Time Longest- Common-Prefix Computation in Suffix Arrays and Its Applications. In Proc. CPM, pp , [11] Phillip Koehn, Franz Josef Och, and Daniel Marcu. Statistical Phrase-Based Translation. In Proc. HLT, pp , [12] Kenneth Heafield. KenLM: Faster and Smaller Language Model Queries. In Proc, WMT, July [13] Franz Josef Och. Minimum Error Rate Training in Statistical Machine Translation. In Proc. ACL, pp , [14] Yusuke Oda, Graham Neubig, Sakriani Sakti, Tomoki Toda, and Satoshi Nakamura. Ckylark: A More Robust PCFG-LA Parser. In Proc. NAACL, pp , June c 2015 Information Processing Society of Japan 7
(Statistical Machine Translation: SMT[1]) [2]
![(Statistical Machine Translation: SMT[1]) [2]](/thumbs/71/65378195.jpg "(Statistical Machine Translation: SMT[1]) [2]")
1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 2 1. (Statistical Machine Translation: SMT[1]) [2] [3] [4][5][6] 2 1 (a) 3 approach 1 Nara Institute of Science and Technology a) miura.akiba.lr9@is.naist.jp
1 n-gram n-gram n-gram [11], [15] n-best [16] n-gram. n-gram. 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e)
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1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 1. [11], [15] 1 Nara Institute of Science and Technology a) akabe.koichi.zx8@is.naist.jp b) neubig@is.naist.jp c) ssakti@is.naist.jp d) tomoki@is.naist.jp
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ISSN1000-0054 CN11-2223/N ( ) 2014 54 12 JTsinghuaUniv(Sci& Technol), 2014,Vol.54, No.12 4/20 1529-1533,, (,, (), 100084) [1-2] :,,,,,,,, :, 0.3~ [3] 0.8BLEU,, : ; ; [4], ; :TP391.2 :A, :1000-0054(2014)12-1529-05,
(String-to-Tree ) KJ [11] best 1-best 2. SMT 2. [9] Brockett [2] Mizumoto [10] Brockett [2] [10] [15] ê = argmax e P(e f ) = argmax e M m=1 λ
![(String-to-Tree ) KJ [11] best 1-best 2. SMT 2. [9] Brockett [2] Mizumoto [10] Brockett [2] [10] [15] ê = argmax e P(e f ) = argmax e M m=1 λ](/thumbs/89/97909699.jpg "(String-to-Tree ) KJ [11] best 1-best 2. SMT 2. [9] Brockett [2] Mizumoto [10] Brockett [2] [10] [15] ê = argmax e P(e f ) = argmax e M m=1 λ")
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: Active Learning 2017/11/12 Contents 0.1 Introduction............................................ 2 0.2..................................... 2 0.2.1 Membership Query Synthesis..............................
[15], [16], [17] [6] [2] [5] Jiang [6] 2.1 [6], [10] Score(x, y) y ( 1) ( 1 ) b e ( 1 ) b e. O(n 2 ) 2.3. 2.2 Jiang [6] (word lattice reranking)
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FX,a),b),c) Bailey Double-Double [] FMA FMA [6] FX FMA SIMD Single Instruction Multiple Data 5 4.5. [] Bailey SIMD SIMD 8bit FMA (SpMV Sparse matrix and vector product) FX. DD Bailey Double-Double a) em49@ns.kogakuin.ac.jp
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