taro.watanabe at nict.go.jp
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- けいざぶろう ひがき
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1 taro.watanabe at nict.go.jp
2 ... I want to study about machine translation. I need to master machine translation. machine translation want to study.
3 infobox infobox buddhist kigen joyo-daishi _ ( ) 686 tooth brushing cleaning
4 : : : : ()
5 ? f = log Pr( e) log Pr(e) log Pr(f, ) I want to study about machine translation I need to master machine translation machine translation want to study I don t want to learn anything kbest
6 ê = e Pr(f,, e) Pr( e) Pr(e) : ê = arg max e arg max e,d d e,d exp w h(f, d, e) exp w h(f, d, e ) w h(f, d, e) h(e,d, f) log-linear = w
7 MT kyoto-train.{ja,en} (?) (?) kyoto-tune.{ja,en}
8 ŵ = arg min w W = arg min w W E Pr(F,E) [ (F, E; w)] (F, E; w)+ (w) (F, E) " " (Ω)
9 kbest F, E C = {} t =1...T w C w (t+1) = w (F, E, C; w)+ (w) (Och and Ney, 2002) k-best?
10 kbest k-best...
11 Histogram of test set BLEU s (Clark et al., 2011) MERT MERT
12 h(f, d, e) = ?
13 ... w
14 F, E = f (i), e (i) N w (1) t {1,...,T} (t), F Ẽ(t) F, E ( F (j), w (t) ) : mini batch w (t+1) w W ( F (t), Ẽ(t), C (t) ; w)+ (w) (T +1) w : etc. i=1 (Watanabe et al., 2007)
15 : BLEU e 1 e 2 e 3 e 4 I want to study about machine translation I need to master machine translation machine translation want to study I don t want to learn anything k-best BLEU BLEU
16 BLEU GEN(f (s), w) e (1),, c (s) 1... c (s) ị.. c (s) K,, e (S) BLEU (1- bestoracle) (Watanabe et al., 2007)
17 BLEU b 0.9 (b + c(e)) l 0.9 (l + f ) B(e) =(l + f ) Bleu(b + c(e)) ê (s) = argmax e ė (s) = argmax e B(e)+w h(f (s), e) +B(e)+w h(f (s), e) sentence-bleu BLEU ( 0.9)(Chiang et al., 2008)
18 F, E = f (i), e (i) N w (1) 0 t {1...T} f, e F, E c (f, w (t) ) 1-best ê, ˆd c e, d õ c e, d = ê, ˆd w (t+1) w (t) + h(f, e, d ) h(f, ê, ˆd) w (T +1) 1 T T +1 t=2 w(t) i=1 or
19 e 3 e 2 e 1 e 4 machine translation want to study I need to master machine translation I want to study about machine translation I don t want to learn anything arg min w max 0, 0 w h(f, e 1 ) w h(f, e 3 ) e1e3 w
20 hinge e 3 e 2 e 1 e 4 machine translation want to study I need to master machine translation I want to study about machine translation I don t want to learn anything arg min w max 0, 1 w h(f, e 1 ) w h(f, e 3 ) e1e31 w
21 MIRA arg min w 1 2 w w(t) 2 + error(e 1, e 3 ) w h(f, e 1 ) w h(f, e 3 ) (Crammer et al., 2006) w (t) e1e3 w
22 MIRA w (t+1) w (t) + (t) h(f, e 1, e 3 ) (t) =min 1, error(e 1, e 3 ) w (t) h(f, e 1, e 3 ) h(f, e 1, e 3 ) 2 h(f, e 1, e 3 ) = h(f, e 1 ) h(f, e 3 ) : α (t) α (t) =1: CW AROW
23 e 3 e 2 e 1 e 4 machine translation want to study I need to master machine translation I want to study about machine translation I don t want to learn anything error(e 1, e 3 ) w (t) h(f, e 1, e 3 ) error(e 2, e 3 ) w (t) h(f, e 2, e 3 ) (Chiang et al., 2008; Chiang et al., 2009)
24 - cost - cost score (Gimpel and Smith, 2012) Figure 1: Hypothetical output space of a translation model for an input sentence x (i). E single translation/derivation output pair. Horizontal bands are caused by output pairs wi hence the same cost) but different derivations. The left plot shows the entire output space outputs in the k-best list. Choosing the output with the lowest cost in the k-best list is sim
25 SGD w (t+1) w (t) (t+1) ( F (t), Ẽ(t), C (t) ; w)+ (w) hinge η
26 AdaGrad g (t+1) g (t) + ( F (t), Ẽ(t), C (t) ; w) 2 w (t+1) w (t) 0 g (t+1) ( F (t), Ẽ(t), C (t) ; w)+ (w) (Duchi et al., 2011) RDA AdaDec AdaDelta
27 F, E { F 1,E 1,..., F S,E S } ( F, E ) s {1,...,S} w s ( F s,e s ) ( F s,e s ) w ({w 1,...,w S }) w shard shard shard
28 shard1shard2shard3shard4shard5 t t + 1 (McDonald et al., 2010)
29
30 : MIRA SGD AdaGrad
31 David Chiang, Yuval Marton, and Philip Resnik Online large-margin training of syntactic and structural translation features. In Proc. of EMNLP 2008, pages , Honolulu Hawaii, October. David Chiang, Kevin Knight, and Wei Wang ,001 new features for statistical machine translation. In Proc. of NAACL-HLT 2009, pages , Boulder, Colorado, June Jonathan H. Clark, Chris Dyer, Alon Lavie, and Noah A Smith Better hypothesis testing for statistical machine translation: Controlling for optimizer instability. In Proceed ings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages , Portland, Oregon, USA, June. Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, and Yoram Singer Online passive-aggressive algorithms. Journal of Machine Learning Research, 7: March. John Duchi, Elad Hazan, and Yoram Singer Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res., 12: , July.
32 Kevin Gimpel and Noah A. Smith Structured ramp loss minimization for machine translation. In Proceedings of the 2012 Conference of the North American Chapter of the As sociation for Computational Linguistics: Human Language Technologies, pages , Montréal, Canada, June. Ryan McDonald, Keith Hall, and Gideon Mann Distributed training strategies for the structured perceptron In Proc. of NAACL-HLT 2010, pages , Los Angeles California, June. Franz Josef Och and Hermann Ney Discriminative training and maximum entropy models for statistica machine translation. In Proc. of ACL 2002, pages Philadelphia, Pennsylvania, USA, July. Taro Watanabe, Jun Suzuki, Hajime Tsukada, and Hidek Isozaki Online Large-Margin Training for Statistica Machine Translation. In Proc. of EMNLP-CoNLL 2007, pages , Prague, Czech Republic, June.
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