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)

HTML
DOWNLOAD

Μέγεθος: px

Εμφάνιση ξεκινά από τη σελίδα:

Download "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)"

Ξένα Δράκος
9 χρόνια πριν
Προβολές:

1 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 e) s-nakamura@is.naist.jp 1 n-gram 1-gram 2-gram the 61 (BOS) the 42, 47. (EOS) 41 and 43, and 32 of 42 of the 27 : 42 in the 21 1 n-gram n-gram n-gram n-best [16] n-gram n-gram L1 c 2012 Information Processing Society of Japan 1

naist.jp d) tomoki@is.naist.jp e) s-nakamura@is.naist.jp 1 n-gram 1-gram 2-gram the 61 (BOS) the 42, 47.

2 2 n-best n-best n-best 1-best n-best 2. n-gram n-gram n-gram n-gram n-gram n-gram ( 1 ) n-gram ( 2 ) n-gram 1-best 1 ( 3 ) n-gram ( 4 ) n-gram n-gram n-gram Ja En(Ref) kyo-chan -lrb- city bus -rrb- En(MT) kyoto chan -lrb- kyoto city bus -rrb- Rules SYMP ( x0:sym SYMP ( NP ( NN ( ) NN ( ) ) x1:sym ) ) x0 kyoto city bus x1 Eval n-gram 3 n-gram (x) p(x, c = e) p(x, c = c) p(x) p( x, c = e) p( x, c = c) p( x) p(c = e) p(c = c) best E k C k n-gram n-gram R k Ek n-best R k EV ( BLEU+1[5]) EV n-gram 1-best 3.1 n-gram F k F E k C k C k n-gram x E k # k (x) e k (x) # k (x) x / C k e k (x) = 0 (otherwise) e k ϕ e (x) = k e k(x) ϕ e n-gram 3.2 n-gram n-gram x n-gram c 2012 Information Processing Society of Japan 2

Eval 1 4 5 n-gram 3 n-gram (x) p(x, c = e) p(x, c = c) p(x) p( x, c = e) p( x, c = c) p( x) p(c = e) p(c = c) 1.

3 PMI PMI [1] P MI(x) = log p(x, c = e) p(x) p(c = e) p 3 p(x) n-gram x p(c = e) n-gram p(x, c = e) x PMI S(x) = p(x, c = e) log p(x, c = e) p(x) p(c = e) (1) n-gram (1) S(x) 3.3 n-gram n-gram F k F E k C k n-gram E k e k (x) C k c k (x) ϕ e(x) = k e k (x) ϕ c = k c k (x) n-gram n-gram p(c = e w = x) = ϕ e(x) ϕ e(x) + ϕ c(x) 1 n-gram 1 MacKay [6] n-gram x (2) S(x) n-gram S(x) = ϕ e(x) + αp e ϕ e(x) + ϕ c(x) + α x P e = ϕ e(x) x ϕ e(x) + x ϕ c(x) (2) α n-gram [13] (3) P = x c {e,c} #(w=x,c) k=0 (k + αp (c)) #(w=x) k=0 (k + α) (3) (3) P α P α P 4. n-gram [16] n-gram F = {F 1,..., F K } n-best Ê = {Ê 1,..., Ê K } R = {R 1,..., R K } Ê k = {Êk,1, Êk,2,..., Êk,I} ϕ(êk,i) w ϕ(êk,i) w n-best E k w 4.1 w [2] Ek Êk ϕ(êk) ϕ(ek ) w Ek Êk 0 F N 4.2 L1 [14] [12] L1 w L1 w 1 = i w i 0 L1 (FOBOS) [3] FOBOS 4.3 : ( 1 ) ϕ s : n-best ( 2 ) n-gram ϕ n : n-gram n-gram ( 3 ) ϕ l : c 2012 Information Processing Society of Japan 3

S(x) = ϕ e(x) + αp e ϕ e(x) + ϕ c(x) + α x P e = ϕ e(x) x ϕ e(x) + x ϕ c(x) (2) α n-gram [13] (3) P = x c {e,c} #(w=x,c) k=0 (k + αp (c)) #(w=x) k=0 (k + α) (3) (3) P α P α P 4.

4 3 KFTT Train 330k 5.91M 6.09M Dev k 26.8k Test k 28.5k Ja En(Ref) En(MT) Eval he was the 1st seii taishogun of the muromachi shogunate. he was the first seii taishogun of the muromachi bakufu -lrb- japanese feudal government headed by a shogun -rrb-. 2 n-gram 4.4 n-best 1-best 1-best Êk R k {Êk, R k } n-best ϕ s 1-best n-gram x n-gram (4) W (x) = #(x, state = error) #(x) (4) #(x, state = error) n-gram #(x) n-gram n-gram 5.1 (KFTT)[7] 3 Travatar [8] Forest-to-String Nile *1 Egret *2 MERT [9] BLEU[10] 3 4 *1 *2 ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) 2 n-best n-best 4.2 FOBOS L KFTT BLEU n-gram 1-gram 3-gram n-best BLEU+1 RIBES[4] 2 n-gram n-gram n-gram 1 1 n-gram n-gram 1 n-gram 2 bakufu -lrb- japanese japanese feudal government 1 2 n-gram bakufu -lrb- japanese feudal government 1 n-gram 5.2 n-gram n-gram c 2012 Information Processing Society of Japan 4

2 n-gram x n-gram (4) W (x) = #(x, state = error) #(x) (4) #(x, state = error) n-gram #(x) n-gram n-gram 5.

5 4 n-gram 3 3 oracle ref oracle ref oracle ref n-best ref n-best 3 n-best 2 n-best n-gram n-best n-best n-gram n-best 1-best n-best 1-best n-best n-gram 5.3 n-gram n-gram 5 1-best 5 n-gram dev test oracle ref oracle ref oracle ref n-best ref n-gram 4 n-gram n-best n-gram n-best n-gram n-gram 1-best 1-best n-gram n-gram 5.4 n-best n-gram BLEU RIBES 2 n-gram 6 BLEU RIBES BLEU RIBES c 2012 Information Processing Society of Japan 5

016 0.024 0.011 0 0 0.181 4 n-best 3 n-best 2 n-best n-gram n-best n-best n-gram n-best 1-best n-best 1-best n-best n-gram 5.3 n-gram n-gram 5 1-best 5 n-gram dev test 1.1 0.2 oracle 381.0 402.

6 6 +BLEU +RIBES RIBES BLEU 6. n-gram n-best n-gram n-best recall n-best 3 [7] Neubig, G.: The Kyoto Free Translation Task, (2011). [8] Neubig, G.: Travatar: A Forest-to-String Machine Translation Engine based on Tree Transducers, Proc. ACL (2013). [9] Och, F. J.: Minimum Error Rate Training in Statistical Machine Translation, Proc. ACL (2003). [10] Papineni, K., Roukos, S., Ward, T. and Zhu, W.-J.: BLEU: a method for automatic evaluation of machine translation, Proc. ACL, pp (2002). [11] Popovic, M. and Ney, H.: Towards automatic error analysis of machine translation output, Computational Linguistics, pp (2011). [12] Roark, B., Saraclar, M. and Collins, M.: Discriminative n-gram language modeling, Computer Speech & Language, Vol. 21, No. 2, pp (2007). [13] Teh, Y. W., Jordan, M. I., Beal, M. J. and Blei, D. M.: Hierarchical Dirichlet processes, Journal of the American Statistical Association, Vol. 101, No. 476 (2006). [14] Tsuruoka, Y., Tsujii, J. and Ananiadou, S.: Stochastic Gradient Descent Training for L1-regularized Log-linear Models with Cumulative Penalty, Proc. ACL, pp (2009). [15] Vilar, D., Xu, J., D Haro, L. F. and Ney, H.: Error analysis of statistical machine translation output, Proc. LREC, pp (2006). [16] Neubig, G. Sakti, S. 20 (NLP2014) pp (2014). [1] Church, K. W. and Hank, P.: Word association norms, mutual information, and lexicography, Computational Linguistics, Vol. 10, pp (1990). [2] Collins, M.: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms, Proc. EMNLP, pp. 1 8 (2002). [3] Duchi, J. and Singer, Y.: Efficient Online and Batch Learning using Forward Backward Splitting, Journal of Machine Learning Research, Vol. 10 (2009). [4] Isozaki, H., Hirao, T., Duh, K., Sudoh, K. and Tsukada, H.: Automatic Evaluation of Translation Quality for Distant Language Pairs, Proc. EMNLP, pp (2010). [5] Lin, C.-Y. and Och, F. J.: Orange: a method for evaluating automatic evaluation metrics for machine translation, Proc. COLING, pp (2004). [6] Mackay, D. J. and Petoy, L. C. B.: A Hierarchical Dirichlet Language Model, Natural Language Engineering, Vol. 1 (1995). c 2012 Information Processing Society of Japan 6

J.: Minimum Error Rate Training in Statistical Machine Translation, Proc. ACL (2003). [10] Papineni, K., Roukos, S., Ward, T. and Zhu, W.-J.

Σχετικά έγγραφα

(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