2. Bilingual Pivoting Bilingual Pivoting [5] e 1 f f e 2 e 1 e 2 p(e 2 e 1 ) p(f e 1 ) p(e 2 f) p(e 2 e 1 ) = f p(e 2 f, e 1 ) p(f e 1 ) f p(e 2 f) p(
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1 Bilingual Pivoting 1,a) 1,b) 2,c) PPDB PPDB Bilingual Pivoting Bilingual Pivoting Bilingual Pivoting Bilingual Pivoting MRR MAP Bilingual Pivoting 1. PPDB [1, 2] [3] [4] PPDB 1 Bilingual Pivoting [5] Bilingual Pivoting LocalPMI [6] [7] Bilingual Pivoting Bilingual Pivoting 1 2 a) kajiwara-tomoyuki@ed.tmu.ac.jp b) komachi@tmu.ac.jp c) daichi@ism.ac.jp 1 Bilingual Pivoting [1] Bilingual Pivoting MRR MAP Bilingual Pivoting 4 Bilingual Pivoting Kneser-Ney Smoothing Bilingual Pivoting *1 *1 1
2 2. Bilingual Pivoting Bilingual Pivoting [5] e 1 f f e 2 e 1 e 2 p(e 2 e 1 ) p(f e 1 ) p(e 2 f) p(e 2 e 1 ) = f p(e 2 f, e 1 ) p(f e 1 ) f p(e 2 f) p(f e 1 ) (1) PPDB [1] (1) Bilingual Pivoting s bp (e 1, e 2 ) λ 1 = λ 2 = 1 *2 s bp (e 1, e 2 ) = λ 1 log p(e 2 e 1 ) λ 2 log p(e 1 e 2 ) = log p(e 2 e 1 ) + log p(e 1 e 2 ) (2) Bilingual Pivoting Kneser-Ney Smoothing [8] Bilingual Pivoting Bilingual Pivoting p(e 2 e 1 ) α y p(y x) ˆp y x n(x) x n(y x) + α y p(y x) = y (n(y x) + α y) n(y x) = n(x) + y α α y 1 y n(x) = n(x) + y α y n(y x) n(x) n(x) = n(x) + y α ˆp y x y (3) y α y n(x) (3) ˆp y x *2 PPDB *3 λ 1 = λ 2 = 1 *3 N-gram Kneser-Ney Smoothing [8] Bilingual Pivoting p kn (e 2 e 1 ) = n(e 2 e 1 ) δ + γ(e 1 )p kn (e 2 ) n(e 1 ) N 1 δ = N 1 + 2N 2 γ(e 1 ) = δ n(e 1 ) N(e 1) p kn (e 2 ) = N(e 2) i N(e i) (4) N n n N(e 1 ) e Bilingual Pivoting p(e 1 ) p(e 2 ) (2) Bilingual Pivoting s pmi (e 1, e 2 ) = log p(e 2 e 1 ) + log p(e 1 e 2 ) log p(e 1 ) log p(e 2 ) (5) (5) s pmi (e 1, e 2 ) = log p(e 2 e 1 ) p(e 2 ) = 2PMI(e 1, e 2 ) + log p(e 1 e 2 ) p(e 1 ) (6) PMI(x, y) p(x, y) PMI(x, y) = log p(x)p(y) p(y x)p(x) = log p(x)p(y) = log p(x y)p(y) p(x)p(y) = log p(y x) p(y) = log p(x y) p(x) (7) p(x, y) = p(y x)p(x) p(x, y) = p(x y)p(y) (6) PMI(x, y) = 1 2 PMI(x, y) + 1 PMI(x, y) 2 = 1 p(y x) log + 1 p(x y) log 2 p(y) 2 p(x) [ {p(y } 1 { } 1 ] x) 2 p(x y) 2 = log p(y) p(x) (8) 2
3 2 MRR: Kneser-Ney Smoothing 3 MAP: Kneser-Ney Smoothing Bilingual Pivoting (6) p(e 2 e 1 ) e 1 e 2 (1) (1) Bilingual Pivoting e 1 e 2 Mixture Model (8) Product Model [9] PPDB [1, 2] Product Model 4.2 [10] LocalPMI [6] LocalPMI(x, y) = n(x, y) PMI(x, y) (9) (9) n(x, y) LocalPMI [11, 12] LocalPMI s lpmi (e 1, e 2 ) = cos(e 1, e 2 ) s pmi (e 1, e 2 ) = cos(e 1, e 2 ) 2PMI(e 1, e 2 ) (10) cos(e 1, e 2 ) e 1 e 2 (10) (6) Bilingual Pivoting Bilingual Pivoting Bilingual Pivoting Europarl-v7 [13] *4 GIZA++ [14] IBM model 4 p(e 2 e 1 ) p(e 1 e 2 ) English Gigaword 5th Edition *5 KenLM [15] p(e 1 ) p(e 2 ) cos(e 1, e 2 ) word2vec [16] cbow *6 Kneser-Ney Smoothing e 1 = e 2 170,682,871 *4 *5 *6 3
4 4 MRR: 5 MAP: 5.2 MRR MAP Pavlick et al. [2] Mean Reciprocal Rank (MRR) Mean Average Precision (MAP) Pavlick et al. [2] Human Paraphrase Judgments *7 Wikipedia Bilingual Pivoting Kneser-Ney Smoothing MRR MAP k 2 3 Kneser-Ney Smoothing Bilingual Pivoting Bilingual Pivoting Kneser-Ney Smoothing 4 5 MRR MAP Bilingual Pivoting PPDB k Bilingual Pivoting 4 MRR Bilingual Pivoting *7 6 k 5 Bilingual Pivoting Bilingual Pivoting 5 MAP Bilingual Pivoting 5.3 Coverage 5.2 k as Bilingual Pivoting 50,000 4
![4 MRR MAP Coverage Pavlick et al. [2] Pavlick et al.](/docs-images/93/111386814/images/5-2.jpg "[2] Human Paraphrase Judgments *4 PPDB [1] 26,456 5 7")
5 7 ρ : p(e 2 e 1 ) 8 ρ : log p(e 2 e 1 ) + log p(e 1 e 2 ) 9 ρ : 2PMI(e 1, e 2 ) 10 ρ : cos(e 1, e 2 ) 11 ρ : cos(e 1, e 2 )2PMI(e 1, e 2 ) 6 Bilingual Pivoting 5.2 p(e 2 e 1 ) Bilingual Pivoting 5.4 MRR MAP Coverage Pavlick et al. [2] Pavlick et al. [2] Human Paraphrase Judgments *4 PPDB [1] 26, PPDB Bilingual Pivoting false positive cultural Bilingual Pivoting cultural PPDB 1 2 cultural 10 3 [3] [4] PPDB 5
6 1 cultural p(e 2 e 1 ) log p(e 2 e 1 ) + log p(e 1 e 2 ) 2PMI(e 1, e 2 ) cos(e 1, e 2 ) cos(e 1, e 2 )2PMI(e 1, e 2 ) 1. diverse culturally culturally-based historical socio-cultural 2. harvests culture culturaldevelopment culture culture 3. firstly 151 cultural-social educational multicultural 4. understand charter economic-cultural linguistic intercultural 5. flowering monuments culture- multicultural educational 6. trying art cultural-educational cross-cultural intellectual 7. structure casal kulturkampf diversity culturally 8. january kahn cultural-political technological sociocultural 9. culture 13 multiculture intellectual heritage 10. culturally caning culturally preservation architectural 2 labourers p(e 2 e 1 ) log p(e 2 e 1 ) + log p(e 1 e 2 ) 2PMI(e 1, e 2 ) cos(e 1, e 2 ) cos(e 1, e 2 )2PMI(e 1, e 2 ) 1. workers 9. gardeners 10. workmen 2. workers 2. workers 2. employees 42. harvesters 11. wage-earners 8. people 4. workmen 9. farmers 62. workers 16. earners 10. persons 5. craftsmen 13. labour 71. seafarers 19. workers 11. farmers 6. wage-earners 16. gardeners 73. unions 21. craftsmen 15. craftsmen 9. persons 17. people 99. homeworkers 22. workforces 26. wage-earners 12. employees 28. workmen 283. works 26. employed 27. workmen 13. earners 30. employed 394. workmen 27. employees 29. harvesters 15. farmers 33. craftsmen 395. employees 50. labour 31. seafarers 18. people 59. harvesters 412. wage-earners 55. persons 32. employees 19. workforces 80. work 415. craftsmen 75. farmers 42. gardeners 37. harvesters 88. earners 417. earners 103. homeworkers 47. earners 42. individuals 90. wage-earners 419. labour 105. individuals 55. workforces 53. labour 106. persons 420. employed 112. work 57. individuals 55. seafarers 109. individuals 431. people 135. people 79. unions 65. gardeners 114. seafarers 433. farmers 187. harvesters 103. labour 88. employed 115. unions 446. workforces 273. gardeners 140. homeworkers 100. homeworkers 131. workforces 451. work 317. seafarers 144. work 105. work 166. homeworkers 453. persons 456. unions 170. employed 149. unions 401. works 474. individuals 469. works 222. works 254. works 2 labourers Bilingual Pivoting SemEval [17 21] 5 5 SemEval-2015 [20] DLS@CU [3] DLS@CU PPDB [22] (11) sts(s 1, s 2 ) = n a(s 1 ) + n a (s 2 ) n(s 1 ) + n(s 2 ) (11) n(s) s n a (s) DLS@CU PPDB 10 3 ALL
7 3 p(e 2 e 1 ) log p(e 2 e 1 ) + log p(e 1 e 2 ) 2PMI(e 1, e 2 ) cos(e 1, e 2 ) cos(e 1, e 2 )2PMI(e 1, e 2 ) STS STS STS STS STS ALL Levy and Goldberg [23] Mikolov et al. [16] skip-gram with negative-sampling SGNS Shifted Positive PMI Bannard and Callison-Burch [5] Bilingual Pivoting PMI Bhagat and Ravichandran [24] PMI 1 c PMI V PMI(e i, c) = log p(e i, c) p(e i )p(c) cos(e i, e j ) = V i V j V i V j (12) PMI PMI Chan et al. [11] Bilingual Pivoting LocalPMI Bilingual Pivoting [5] Ganitkevitch and Callison-Burch [25] 23 *8 Mizukami et al. [26] *9 Bilingual Pivoting *8 *9 Bilingual Pivoting PPDB [1] [3] [4] [27] [28] [29] [30] Bilingual Pivoting PPDB 8. Bilingual Pivoting MRR MAP Bilingual Pivoting [1] Ganitkevitch, J., Van Durme, B. and Callison-Burch, C.: PPDB: The Paraphrase Database, Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp (2013). [2] Pavlick, E., Rastogi, P., Ganitkevitch, J., Van Durme, B. and Callison-Burch, C.: PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp (2015). [3] Sultan, M. A., Bethard, S. and Sumner, T.: DLS@CU: Sentence Similarity from Word Alignment and Semantic Vector Composition, Proceedings of the 9th International Workshop on Semantic Evaluation, pp (2015). [4] Yu, M. and Dredze, M.: Improving Lexical Embeddings with Semantic Knowledge, Proceedings of the 52nd Annual Meeting of the Association for Computational Lin- 7
8 guistics, pp (2014). [5] Bannard, C. and Callison-Burch, C.: Paraphrasing with Bilingual Parallel Corpora, Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pp (2005). [6] Evert, S.: The Statistics of Word Cooccurrences: Word Pairs and Collocations, PhD Thesis, University of Stuttgart (2005). [7] Mohammad, S. M., Dorr, B. J., Hirst, G. and Turney, P. D.: Computing Lexical Contrast, Computational Linguistics, Vol. 39, No. 3, pp (2013). [8] Kneser, R. and Ney, H.: Improved Backing-off for M- gram Language Modeling, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 1, pp (1995). [9] Hinton, G. E.: Training Products of Experts by Minimizing Contrastive Divergence, Neural Computation, Vol. 14, No. 8, pp (2002). [10] Levy, O., Goldberg, Y. and Dagan, I.: Improving Distributional Similarity with Lessons Learned from Word Embeddings, Transactions of the Association for Computational Linguistics, Vol. 3, pp (2015). [11] Chan, T. P., Callison-Burch, C. and Van Durme, B.: Reranking Bilingually Extracted Paraphrases Using Monolingual Distributional Similarity, Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics, pp (2011). [12] Glavaš, G. and Štajner, S.: Simplifying Lexical Simplification: Do We Need Simplified Corpora?, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp (2015). [13] Koehn, P.: Europarl: A Parallel Corpus for Statistical Machine Translation, Proceedings of the Machine Translation Summit, pp (2005). [14] Och, F. J. and Ney, H.: A Systematic Comparison of Various Statistical Alignment Models, Computational Linguistics, Vol. 29, No. 1, pp (2003). [15] Heafield, K.: KenLM: Faster and Smaller Language Model Queries, Proceedings of the Sixth Workshop on Statistical Machine Translation, pp (2011). [16] Mikolov, T., Chen, K., Corrado, G. S. and Dean, J.: Efficient Estimation of Word Representations in Vector Space, Proceedings of Workshop at the International Conference on Learning Representations, pp (2013). [17] Agirre, E., Cer, D., Diab, M. and Gonzalez-Agirre, A.: SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity, *SEM 2012: The First Joint Conference on Lexical and Computational Semantics, pp (2012). [18] Agirre, E., Cer, D., Diab, M., Gonzalez-Agirre, A. and Guo, W.: *SEM 2013 shared task: Semantic Textual Similarity, Second Joint Conference on Lexical and Computational Semantics, pp (2013). [19] Agirre, E., Banea, C., Cardie, C., Cer, D., Diab, M., Gonzalez-Agirre, A., Guo, W., Mihalcea, R., Rigau, G. and Wiebe, J.: SemEval-2014 Task 10: Multilingual Semantic Textual Similarity, Proceedings of the 8th International Workshop on Semantic Evaluation, pp (2014). [20] Agirre, E., Banea, C., Cardie, C., Cer, D., Diab, M., Gonzalez-Agirre, A., Guo, W., Lopez-Gazpio, I., Maritxalar, M., Mihalcea, R., Rigau, G., Uria, L. and Wiebe, J.: SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability, Proceedings of the 9th International Workshop on Semantic Evaluation, pp (2015). [21] Agirre, E., Banea, C., Cer, D., Diab, M., Gonzalez- Agirre, A., Mihalcea, R., Rigau, G. and Wiebe, J.: SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation, Proceedings of the 10th International Workshop on Semantic Evaluation, pp (2016). [22] Sultan, M. A., Bethard, S. and Sumner, T.: Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence, Transactions of the Association for Computational Linguistics, Vol. 2, pp (2014). [23] Levy, O. and Goldberg, Y.: Neural Word Embedding as Implicit Matrix Factorization, Advances in Neural Information Processing Systems, pp (2014). [24] Bhagat, R. and Ravichandran, D.: Large Scale Acquisition of Paraphrases for Learning Surface Patterns, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp (2008). [25] Ganitkevitch, J. and Callison-Burch, C.: The Multilingual Paraphrase Database, Proceedings of the Ninth International Conference on Language Resources and Evaluation, pp (2014). [26] Mizukami, M., Neubig, G., Sakti, S., Toda, T. and Nakamura, S.: Building a Free, General-Domain Paraphrase Database for Japanese, Proceedings of the 17th Oriental COCOSDA Conference, pp (2014). [27] Mehdizadeh Seraj, R., Siahbani, M. and Sarkar, A.: Improving Statistical Machine Translation with a Multilingual Paraphrase Database, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp (2015). [28] Napoles, C., Callison-Burch, C. and Post, M.: Sentential Paraphrasing as Black-Box Machine Translation, Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics, pp (2016). [29] Sultan, M. A., Castelli, V. and Florian, R.: A Joint Model for Answer Sentence Ranking and Answer Extraction, Transactions of the Association for Computational Linguistics, Vol. 4, pp (2016). [30] Xu, W., Napoles, C., Pavlick, E., Chen, Q. and Callison- Burch, C.: Optimizing Statistical Machine Translation for Text Simplification, Transactions of the Association for Computational Linguistics, Vol. 4, pp (2016). 8
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