Vol. 23 No. 5 December (Rule-Based Machine Translation; RBMT (Nirenburg 1989)) 2 (Statistical Machine Translation; SMT (Brown, Pietra, Piet
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1 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 pivot translation approach allows for translation of language pairs with little or no parallel data by introducing a third language for which data exists. In particular, the triangulation method, which translates by combining source-pivot and pivot-target translation models into a source-target model is known for its high translation accuracy. However, in the conventional triangulation method, information of pivot phrases is forgotten, and not used in the translation process. In this research, we propose a novel approach to remember the pivot phrases in the triangulation stage, and use a pivot language model as an additional information source at translation phase. Experimental results on the united nations parallel corpus showed significant improvements in all tested combinations of languages. Key Words: Statistical Machine Translation, Multilinguality, Pivot Translation, Synchronous Context-Free Grammars, Language Models, Parallel Corpora, Graduate School of Information Science, Nara Institute of Science and Technology, Language Technologies Institute, Carnegie Mellon University, Information Technology Center, Nagoya University
2 Vol. 23 No. 5 December (Rule-Based Machine Translation; RBMT (Nirenburg 1989)) 2 (Statistical Machine Translation; SMT (Brown, Pietra, Pietra, and Mercer 1993)) 2 SMT (Dyer, Cordova, Mont, and Lin 2008) (Pvt) (de Gispert and Mariño 2006; Cohn and Lapata 2007; Zhu, He, Wu, Zhu, Wang, and Zhao 2014) 2 (Cascade Translation (de Gispert and Mariño 2006)) (Src-Pvt) (Pvt-Trg) 2 SMT (Src-Trg) SMT (Triangulation (Cohn and Lapata 2007)) (Utiyama and Isahara 2007) SMT (Phrase-Based Machine Translation; PBMT (Koehn, Och, and Marcu 2003)) 2
3 Neubig Sakti,, PBMT SMT (Synchronous Context-Free Grammar; SCFG (Chiang 2007)) SMT PBMT PBMT SCFG Src-Trg 近似 approach approach approccio アプローチ approximation approximation accesso 接近 access (a) - (Src-Pvt ) access ravvicinamento (b) - (Pvt-Trg ) 近似 approccio 近似 approccio アプローチ accesso アプローチ accesso 接近 ravvicinamento 接近 ravvicinamento (c) - (d) Src-Pvt Pvt-Trg Src-Trg 1 (a) (b) (c) (d) 1(c) Src-Trg 3
4 Vol. 23 No. 5 December PBMT SMT 1.1 PBMT SMT SCFG SCFG PBMT 1 2 1(c) 1.1 SMT ( ) SMT ( ) 1 2 SMT (2.1 ) SMT (PBMT, 2.2 ) (SCFG, 2.3 ) SCFG 3 1 ( Neubig Sakti 2014, 2015) ACL 2015: The 53rd Annual Meeting of the Association for Computational Linguistics (Miura, Neubig, Sakti, Toda, and Nakamura 2015) 4
5 Neubig Sakti,, (Multi-Synchronous Context-Free Grammar; MSCFG, 2.4 ) 2.1 SMT (Shannon 1948) f E(f) f e E(f) P r(e f) e SMT P r(e f) ê E(f) ê = arg max P r(e f) (1) e E(f) = arg max e E(f) P r(f e)p r(e) P r(f) = arg max P r(f e)p (e) (3) e E(f) (Och 2003) ê = arg max P r(e f) (4) e E(f) arg max e E(f) exp ( w T h(f, e ) e exp (w T h(f, e )) = arg max w T h(f, e) (6) e E(f) h P r(e) P r(f e) SMT w h w ( ) BLEU(Papineni, Roukos, Ward, and Zhu 2002) (Och 2003) 2.2 (2) (5) 5
6 Vol. 23 No. 5 December Koehn (PBMT (Koehn et al. 2003)) SMT PBMT (Brown et al. 1993) John hit a ball. John hit a ball. ジョンはボールを打った ジョンはボールを打った PBMT PBMT (6) PBMT 2 (Goto, Utiyama, Sumita, Tamura, and Kurohashi 2013) 2 6
7 Neubig Sakti,, 2.3 SMT (SCFG (Chiang 2007)) SCFG (Hierarchical Phrase-Based Translation; Hiero (Chiang 2007)) SCFG X s, t (7) X s t s t X X 0 of X 1, X 1 X 0 (8) Hiero SCFG PBMT X i SCFG 2 X X 0 hit X 1., X 0 X 1 (9) X John, (10) X a ball, (11) S S X 0, X 0 S = X 0, X 0 (12) = X 1 hit X 2., X 1 X 2 (13) = John hit X 2., X 2 (14) = John hit a ball., (15) SCFG ϕ(s t) ϕ(t s) ϕ lex (s t) ϕ lex (t s) (t ) ( 1) 6 7
8 Vol. 23 No. 5 December 2016 CKY+ (Chappelier, Rajman, et al. 1998) (Chiang 2007) 2.4 SCFG (MSCFG (Neubig, Arthur, and Duh 2015)) SCFG t MSCFG N X s, t 1,, t N (16) MSCFG SCFG 3 1 N 1 SCFG PBMT MSCFG 3 3 PBMT MSCFG 8
9 Neubig Sakti,, 3 2 SMT SMT SMT 100 SMT PBMT 4 PBMT SCFG Src Trg Pvt Src-Pvt, Src-Trg, Pvt-Trg 3.1 対訳 Src-Pvt 対訳 Pvt-Trg Src SMT SMT Src Pvt Pvt Pvt Trg Trg 4 (Cascade) (de Gispert and Mariño 2006) Src Trg 4 Src-Pvt, Pvt-Trg Src Pvt Pvt Trg Src Trg 9
10 Vol. 23 No. 5 December 2016 PBMT 2 Src-Pvt n Pvt-Trg (Utiyama and Isahara 2007) n 3.2 対訳 Pvt-Trg 対訳 Src-Pvt SMT Pvt Trg 擬似対訳 Src-Trgʼ Src SMT Src Trg Trg 5 Src-Trg SMT (Synthetic) (de Gispert and Mariño 2006) Src-Trg 5 Src-Pvt, Pvt-Trg Pvt-Trg SMT Src-Pvt Pvt Pvt-Trg Src-Trg Src-Trg SMT SMT De Gispert (de Gispert and Mariño 2006) 10
11 Neubig Sakti,, 対訳 Src-Pvt 対訳 Pvt-Trg SMT Src Pvt SMT Pvt Trg Src SMT Src Trg Trg PBMT SCFG Src-Trg 6 Cohn (Triangulation) (Cohn and Lapata 2007) Src-Pvt Pvt-Trg T SP, T P T T SP, T P T Src-Trg T ST T ST ϕ( ) ϕ lex ( ) ϕ ( t s ) ( ) = t p ϕ (p s) (17) p T SP T P T ϕ ϕ ( s t ) ( ) = ϕ (s p) ϕ p t (18) p T SP T P T ( ) ( ) ϕ lex t s = ϕ lex t p ϕlex (p s) (19) p T SP T P T ( ) ( ) ϕ lex s t = ϕ lex (s p) ϕ lex p t (20) p T SP T P T s, p, t Src, Pvt, Trg p T SP T P T p T SP, T P T (17)-(20) ϕ ( t p, s ) = ϕ ( t p ) (21) ϕ ( s p, t ) = ϕ (s p) (22) 11
12 Vol. 23 No. 5 December 2016 Utiyama (Utiyama and Isahara 2007) n = 1 n = 15 BLEU 4 3 SMT SCFG SMT PBMT SCFG (7) SCFG PBMT PBMT SCFG 4.1 SCFG Src-Pvt, Pvt-Trg (2.3 ) Src-Pvt, Pvt-Trg Pvt X s, p, X p, t X s, t (17)-(20) PBMT s, p, t ( ) SCFG X s, t X p, t PBMT PBMT SCFG 12
13 Neubig Sakti,, (Ziemski, Junczys-Dowmunt, and Pouliquen 2016) (En) (Ar) (Es) (Fr) (Ru) (Zh) 6 1,100 6 SMT 5 Src-Pvt Pvt-Trg Src-Trg Pvt (train) 1,100 (test) 4,000 (dev) 4,000 train 60, test, dev 80 train 800 test, dev 3,800 train Src-Pvt train1 Pvt-Trg train2 10 test dev 1,500 PBMT SCFG SMT Direct ( ): Pvt Src-Trg train1 train2 train1, train2 Direct 1 Direct 2 Direct 1 / 2 Cascade ( ): Src-Pvt, Pvt-Trg train1 train2 Src-Trg Triangulation ( ): Src-Pvt, Pvt-Trg train1, train2 Src-Trg 13
14 Vol. 23 No. 5 December Source Target MT Method Ar Es Fr Ru Zh Es Fr Ru Zh Ar Fr Ru Zh Ar Es Ru Zh Ar Es Fr Zh Ar Es Fr Ru BLEU Score [%] Direct 1 / 2 Triangulation Cascade PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT 8.93 / Hiero / PBMT / Hiero / PBMT / Hiero / PBMT / Hiero /
15 Neubig Sakti,, KyTea (Neubig, Nakata, and Mori 2011) PBMT Moses (Koehn, Hoang, Birch, Callison-Burch, Federico, Bertoldi, Cowan, Shen, Moran, Zens, Dyer, Bojar, Constantin, and Herbst 2007) SCFG Travatar (Neubig 2013) Hiero KenLM (Heafield 2011) train1+train gram BLEU (Papineni et al. 2002) SMT MERT (Och 2003) BLEU 4.3 Direct 1 / 2, Triangulation, Cascade 1 PBMT Triangulation Cascade 4.1 SCFG Triangulation Cascade SMT Triangulation Cascade Triangulation Direct Hiero Direct BLEU Triangulation BLEU Direct Triangulation Direct BLEU 15 Triangulation Hiero Triangulation Direct PBMT Hiero Hiero PBMT Hiero Triangulation PBMT 0.5 BLEU Hiero 15
16 Vol. 23 No. 5 December ,,. Europarl (Koehn 2005) Europarl 10 1,500 Src-Pvt Pvt-Trg 4.3 PBMT Hiero Triangulation Cascade 2 Triangulation Direct PBMT Hiero PBMT PBMT SCFG SCFG PBMT PBMT (Utiyama and Isahara 2007) 1 (Zhu et al. 2014) (Levinboim and Chiang 2015) 16
17 Neubig Sakti,, (Dabre, Cromieres, Kurohashi, and Bhattacharyya 2015), 5 3 SMT 4 SCFG SCFG PBMT Src-Trg Src-Pvt, Pvt-Trg Src-Trg Src-Trg 近似 アプローチ 接近 approach approximation access approccio accesso ravvicinamento 7 ( - - ) 7 3 approach Src-Trg 8 Src Trg Pvt 17
18 Vol. 23 No. 5 December 2016 近似 (via: approach) approccio 近似 (via: approach) accesso 近似 ravvicinamento (via: approach, approximation) アプローチ (via: approach) approccio 8 Src-Trg Pvt 5.2 近似 近似 近似 アプローチ approccio, approach ravvicinamento, approach ravvicinamento, approximation approccio, approach 9 Src Trg Pvt Src Trg Pvt 9 SCFG (2.3 ) MSCFG (2.4 ) MSCFG Src-Pvt, Pvt-Trg SCFG 18
19 Neubig Sakti,, SCFG Src-Trg-Pvt MSCFG Pvt SCFG MSCFG SCFG Src-Pvt, Pvt-Trg Pvt X s, p, X p, t X s, t (17)-(20) X s, p, X p, t X s, t, p (23) (17)-(20) Trg Pvt ϕ ( t, p s ) ϕ ( s p, t ) ϕ ( t, p s ) = ϕ ( t p ) ϕ (p s) (24) ϕ ( s p, t ) = ϕ (s p) (25) Src-Pvt ϕ (p s) ϕ (s p) ϕ lex (p s) ϕ lex (s p) T SP 10 ϕ ( t s ) ϕ ( s t ) ϕ (p s) ϕ (s p) ( ) ( ) ϕ lex t s ϕlex s t ϕlex (p s) ϕ lex (s p) ϕ ( t, p s ) ϕ ( s p, t ) t p MSCFG 5.4 s, t s, t, p Neubig T 1 T 2 T 1 - (Neubig et al. 2015) T 1 = T rg T 2 = P vt s T rg ϕ ( t s ) L t t ϕ ( t, p s ) 19
20 Vol. 23 No. 5 December 2016 p (En) (Ar) (Es) (Fr) (Ru) (Zh) 5 Src-Pvt (train1) 10 Pvt-Trg (train2) 10 (test) (dev) 1, train1+train SCFG MSCFG Travatar (Neubig 2013) Hiero SCFG BLEU (Papineni et al. 2002) MERT (Och 2003) BLEU MSCFG L = 20 T 1-6 Cascade ( ): Src-Pvt Pvt-Trg SCFG (3.1 ) w/ PvtLM 200k/5M Src-Pvt Tri. SCFG (SCFG ): Src-Pvt Pvt-Trg SCFG Src-Trg SCFG (3.3 ) Tri. MSCFG (MSCFG ): Src-Pvt Pvt-Trg SCFG Src-Trg-Pvt MSCFG (5 ) w/o PvtLM w/ PvtLM 200k/5M 20
21 Neubig Sakti,, 2 Src Ar Es Fr Ru Zh BLEU Score [%] Trg Cascade Cascade Tri. SCFG Tri. MSCFG Tri. MSCFG Tri. MSCFG w/ PvtLM 200k w/ PvtLM 5M (baseline) w/o PvtLM w/ PvtLM 200k w/ PvtLM 5M Es Fr Ru Zh Ar Fr Ru Zh Ar Es Ru Zh Ar Es Fr Zh Ar Es Fr Ru (Koehn 2004). Tri. SCFG BLEU Tri. SCFG ( : p < 0.05, : p < 0.01) BLEU BLEU MSCFG 21
22 Vol. 23 No. 5 December 2016 BLEU Score [%] 25.0 Translation Accuracy vs. Pivot-LM Size (Zh-Es via En) Direct 1 Tri. SCFG Tri. MSCFG 24.8 Direct Pivot-LM Size [Sent.] BLEU Score [%] 17.6 Translation Accuracy vs. Pivot-LM Size (Ar-Ru via En) Direct 1 Tri. SCFG Tri. MSCFG 17.4 Direct Pivot-LM Size [Sent.] 10 (Tri. MSCFG w/o PvtLM) SCFG,,,500 (Cascade w/ PvtLM 5M). Cascade w/ PvtLM 5M 20 (Cascade w/ PvtLM 200k) Zh-Ar Zh-Ru, Tri. SCFG,., Tri. MSCFG w/ PvtLM 5M,Cascade w/ PvtLM 5M,, ( ) ( ) 22
23 Neubig Sakti,, (Brants, Popat, Xu, Och, and Dean 2007) ( ): Le nom du candidat proposé est indiqué dans l annexa à la présente note. ( ): El nombre del candidato propuesto se presenta en el anexo de la presente nota. : The name of the candidate thus nominated is set out in the annex to the present note. Tri. SCFG: El nombre del proyecto de un candidato se indica en el anexo a la presente nota. (BLEU+1: 34.99) Tri. MSCFG w/ PvtLM 5M: El nombre del candidato propuesto se indica en el anexo a la presente nota. (BLEU+1: 61.13) The name of the candidate proposed indicated in the annex to the present note. ( ) proposé ( ) propuesto proyecto ( ) proposé propuesto proposed ( ): J. Risques d aspiration : citère de viscosité pour la classification des mélanges ; ( ): J. Peligros por aspiración : criterio de viscosidad para la clasificación de mezclas ; 23
24 Vol. 23 No. 5 December Direct F-Measure [%] Tri. MSCFG w/ PvtLM 2M Tri.SCFG NC ( ) 8, (+0.01) P ( ) 6, (+1.02) DET ( ) 5, (+1.50) PUNC ( ) 4, (+0.44) ADJ ( ) 3, (+0.67) V ( ) 3, (+1.06) ADV ( ) 2, (+0.34) : J. Aspiration hazards : viscosity criterion for classification of mixtures ; Direct 1: J. Riesgos d aspiration : criterio de viscosité para la clasificación de los mélanges ; (BLEU+1: 34.20) Direct 2: J. Riesgos d aspiration : criterio de viscosité para la clasificación de mezclas ; (BLEU+1: 49.16) Tri. MSCFG w/ PvtLM 2M: J. Riesgos d aspiration : viscosité criterios para la clasificación de mélanges ; (BLEU+1: 27.61) J. Risk d aspiration : viscosité criteria for the categorization of mélanges ; ( ) d aspiration ( ) mélanges ( ) d aspiration train1 train2 Direct 1 / 2 mélanges train2 Direct 1 2 criterio ( ) criterios 24
25 Neubig Sakti,, 4 Direct F-Measure [%] Tri. MSCFG w/ PvtLM 2M Tri.SCFG NN ( ) 10, (+0.53) ART ( ) 4, (+1.24) CARD ( ) 3, ( 0.44) APPR ( ) 2, (+1.50) ADJA ( ) 2, (+0.47) NE ( ) 2, (+0.47) ADV ( ) 1, (+0.18) PPEF ( ) 1, (+1.67) Europarl 4.4 Europarl Tri. SCFG Cascade Stanford POS Tagger (Toutanova and Manning 2000; Toutanova, Klein, Manning, and Singer 2003) F 3 4 F 3 F F 25
26 Vol. 23 No. 5 December 2016 Direct F Direct Direct F 4 3 F Direct 7 PBMT SCFG 6 26
27 Neubig Sakti,, 1 SCFG 2 JSPS 16H ATR-Trek 27
28 Vol. 23 No. 5 December 2016 Brants, T., Popat, A. C., Xu, P., Och, F. J., and Dean, J. (2007). Large Language Models in Machine Translation. In Proc. EMNLP, pp Brown, P. F., Pietra, V. J., Pietra, S. A. D., and Mercer, R. L. (1993). The Mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics, 19, pp Chappelier, J.-C., Rajman, M., et al. (1998). A Generalized CYK Algorithm for Parsing Stochastic CFG. In Proc. TAPD, Vol. 98, p. 5. Citeseer. Chiang, D. (2007). Hierarchical phrase-based translation. Computational Linguistics, 33 (2), pp Cohn, T. and Lapata, M. (2007). Machine Translation by Triangulation: Making Effective Use of Multi-Parallel Corpora. In Proc. ACL, pp Dabre, R., Cromieres, F., Kurohashi, S., and Bhattacharyya, P. (2015). Leveraging Small Multilingual Corpora for SMT Using Many Pivot Languages. In Proc. NAACL, pp de Gispert, A. and Mariño, J. B. (2006). 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. Dyer, C., Cordova, A., Mont, A., and Lin, J. (2008). Fast, Easy, and Cheap: Construction of Statistical Machine Translation Models with MapReduce. In Proc. WMT, pp Goto, I., Utiyama, M., Sumita, E., Tamura, A., and Kurohashi, S. (2013). Distortion Model Considering Rich Context for Statistical Machine Translation. In Proc. ACL, pp Heafield, K. (2011). KenLM: Faster and Smaller Language Model Queries. In Proc, WMT. Koehn, P. (2004). Statistical Significance Tests for Machine Translation Evaluation. In Lin, D. and Wu, D. (Eds.), Proc. EMNLP, pp Koehn, P. (2005). Europarl: A parallel corpus for statistical machine translation. In MT summit, Vol. 5, pp Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., Dyer, C., Bojar, O., Constantin, A., and Herbst, E. (2007). Moses: Open Source Toolkit for Statistical Machine Translation. In Proc. ACL, pp Koehn, P., Och, F. J., and Marcu, D. (2003). Statistical Phrase-Based Translation. In Proc. NAACL, pp Levinboim, T. and Chiang, D. (2015). Supervised Phrase Table Triangulation with Neural Word Embeddings for Low-Resource Languages. In Proc. EMNLP, pp
29 Neubig Sakti,, Miura, A., Neubig, G., Sakti, S., Toda, T., and Nakamura, S. (2015). Improving Pivot Translation by Remembering the Pivot. In Proc. ACL, pp Neubig, G. (2013). Travatar: A Forest-to-String Machine Translation Engine based on Tree Transducers. In Proc. ACL Demo Track, pp Neubig, G., Arthur, P., and Duh, K. (2015). Multi-Target Machine Translation with Multi- Synchronous Context-free Grammars. In Proc. NAACL, pp Neubig, G., Nakata, Y., and Mori, S. (2011). Pointwise Prediction for Robust, Adaptable Japanese Morphological Analysis. In Proc. ACL, pp Nirenburg, S. (1989). Knowledge-Based Machine Translation. Machine Translation, 4 (1), pp Och, F. J. (2003). Minimum Error Rate Training in Statistical Machine Translation. In Proc. ACL, pp Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002). BLEU: a Method for Automatic Evaluation of Machine Translation. In Proc. ACL, pp Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27 (3), pp Toutanova, K., Klein, D., Manning, C. D., and Singer, Y. (2003). Feature-rich Part-of-speech Tagging with a Cyclic Dependency Network. In Proc. NAACL, pp Toutanova, K. and Manning, C. D. (2000). Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger. In Proc. EMNLP, pp Utiyama, M. and Isahara, H. (2007). A Comparison of Pivot Methods for Phrase-Based Statistical Machine Translation. In Proc. NAACL, pp Zhu, X., He, Z., Wu, H., Zhu, C., Wang, H., and Zhao, T. (2014). Improving Pivot-Based Statistical Machine Translation by Pivoting the Co-occurrence Count of Phrase Pairs. In Proc. EMNLP, pp Ziemski, M., Junczys-Dowmunt, M., and Pouliquen, B. (2016). The United Nations Parallel Corpus v1.0. In Proc. LREC, pp Neubig Graham Sakti Sakriani (2014) (SIG-NL). Neubig Graham Sakti Sakriani (2015) (SIG-NL)
30 Vol. 23 No. 5 December ACL Graham Neubig: Sakriani Sakti: ATR INRIA JNS SFN ASJ ISCA IEICE IEEE PD IEEE ATR 2006 ( ) ATR Antonio Zampoli ISCA IEEE SLTC IEEE 30
Vol. 23 No. 5 December (Rule-Based Machine Translation; RBMT (Nirenburg 1989)) 2 (Statistical Machine Translation; SMT (Brown, Pietra, Piet
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