publication . Preprint . 2016

Transition-based Parsing with Context Enhancement and Future Reward Reranking

Zhou, Fugen; Wu, Fuxiang; Zhang, Zhengchen; Dong, Minghui;
Open Access English
  • Published: 15 Dec 2016
Abstract
This paper presents a novel reranking model, future reward reranking, to re-score the actions in a transition-based parser by using a global scorer. Different to conventional reranking parsing, the model searches for the best dependency tree in all feasible trees constraining by a sequence of actions to get the future reward of the sequence. The scorer is based on a first-order graph-based parser with bidirectional LSTM, which catches different parsing view compared with the transition-based parser. Besides, since context enhancement has shown substantial improvement in the arc-stand transition-based parsing over the parsing accuracy, we implement context enhanc...
Subjects
free text keywords: Computer Science - Computation and Language
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37 references, page 1 of 3

[1] Andor, D., Alberti, C., Weiss, D., Severyn, A., Presta, A., Ganchev, K., Petrov, S., Collins, M.: Globally normalized transition-based neural networks. In: ACL 2016, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers (2016)

[2] Bohnet, B., McDonald, R.T., Pitler, E., Ma, J.: Generalized transition-based dependency parsing via control parameters. In: ACL 2016, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers (2016)

[3] Chen, D., Manning, C.D.: A fast and accurate dependency parser using neural networks. In: EMNLP 2014, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 740-750 (2014)

[4] Chen, Y., Huang, Z., Shi, X.: An snn-based semantic role labeling model with its network parameters optimized using an improved pso algorithm. Neural Processing Letters 44(1), 245-263 (2016)

[5] Cheng, H., Fang, H., He, X., Gao, J., Deng, L.: Bi-directional attention with agreement for dependency parsing. CoRR abs/1608.02076 (2016)

[6] Cheng, J.J., Zhang, X., Li, P., Zhang, S., Ding, Z.Y., Wang, H.: Exploring sentiment parsing of microblogging texts for opinion polling on chinese public figures. Applied Intelligence 45(2), 429-442 (2016). DOI 10.1007/s10489-016-0768-0

[7] Coavoux, M., Crabb, B.: Neural greedy constituent parsing with dynamic oracles. In: ACL 2016, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers (2016) [OpenAIRE]

[8] Corro, C., Roux, J.L., Lacroix, M., Rozenknop, A., Calvo, R.W.: Dependency parsing with bounded block degree and well-nestedness via lagrangian relaxation and branch-and-bound. In: ACL 2016, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers (2016)

[9] Dozat, T., Manning, C.D.: Deep biaffine attention for neural dependency parsing. CoRR abs/1611.01734 (2016) [OpenAIRE]

[10] Dyer, C., Ballesteros, M., Ling, W., Matthews, A., Smith, N.A.: Transition-based dependency parsing with stack long short-term memory. In: ACL 2015, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, July 26-31, 2015, Beijing, China, Volume 1: Long Papers, pp. 334-343 (2015)

[11] Dyer, C., Ballesteros, M., Ling, W., Matthews, A., Smith, N.A.: Transition-based dependency parsing with stack long short-term memory. In: ACL 2015, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, July 26-31, 2015, Beijing, China, Volume 1: Long Papers, pp. 334-343 (2015)

[12] Gers, F.A., Schmidhuber, J., Cummins, F.A.: Learning to forget: Continual prediction with lstm. Neural Computation 12(10), 2451-2471 (2000) [OpenAIRE]

[13] Goldberg, Y., Nivre, J.: A dynamic oracle for arc-eager dependency parsing. In: COLING 2012, 24th International Conference on Computational Linguistics, Technical Papers, 8-15 December 2012, Mumbai, India, pp. 959-976 (2012)

[14] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735- 1780 (1997)

[15] Kiperwasser, E., Goldberg, Y.: Simple and accurate dependency parsing using bidirectional lstm feature representations. TACL 4, 313-327 (2016) [OpenAIRE]

37 references, page 1 of 3
Abstract
This paper presents a novel reranking model, future reward reranking, to re-score the actions in a transition-based parser by using a global scorer. Different to conventional reranking parsing, the model searches for the best dependency tree in all feasible trees constraining by a sequence of actions to get the future reward of the sequence. The scorer is based on a first-order graph-based parser with bidirectional LSTM, which catches different parsing view compared with the transition-based parser. Besides, since context enhancement has shown substantial improvement in the arc-stand transition-based parsing over the parsing accuracy, we implement context enhanc...
Subjects
free text keywords: Computer Science - Computation and Language
Download from
37 references, page 1 of 3

[1] Andor, D., Alberti, C., Weiss, D., Severyn, A., Presta, A., Ganchev, K., Petrov, S., Collins, M.: Globally normalized transition-based neural networks. In: ACL 2016, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers (2016)

[2] Bohnet, B., McDonald, R.T., Pitler, E., Ma, J.: Generalized transition-based dependency parsing via control parameters. In: ACL 2016, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers (2016)

[3] Chen, D., Manning, C.D.: A fast and accurate dependency parser using neural networks. In: EMNLP 2014, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 740-750 (2014)

[4] Chen, Y., Huang, Z., Shi, X.: An snn-based semantic role labeling model with its network parameters optimized using an improved pso algorithm. Neural Processing Letters 44(1), 245-263 (2016)

[5] Cheng, H., Fang, H., He, X., Gao, J., Deng, L.: Bi-directional attention with agreement for dependency parsing. CoRR abs/1608.02076 (2016)

[6] Cheng, J.J., Zhang, X., Li, P., Zhang, S., Ding, Z.Y., Wang, H.: Exploring sentiment parsing of microblogging texts for opinion polling on chinese public figures. Applied Intelligence 45(2), 429-442 (2016). DOI 10.1007/s10489-016-0768-0

[7] Coavoux, M., Crabb, B.: Neural greedy constituent parsing with dynamic oracles. In: ACL 2016, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers (2016) [OpenAIRE]

[8] Corro, C., Roux, J.L., Lacroix, M., Rozenknop, A., Calvo, R.W.: Dependency parsing with bounded block degree and well-nestedness via lagrangian relaxation and branch-and-bound. In: ACL 2016, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers (2016)

[9] Dozat, T., Manning, C.D.: Deep biaffine attention for neural dependency parsing. CoRR abs/1611.01734 (2016) [OpenAIRE]

[10] Dyer, C., Ballesteros, M., Ling, W., Matthews, A., Smith, N.A.: Transition-based dependency parsing with stack long short-term memory. In: ACL 2015, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, July 26-31, 2015, Beijing, China, Volume 1: Long Papers, pp. 334-343 (2015)

[11] Dyer, C., Ballesteros, M., Ling, W., Matthews, A., Smith, N.A.: Transition-based dependency parsing with stack long short-term memory. In: ACL 2015, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, July 26-31, 2015, Beijing, China, Volume 1: Long Papers, pp. 334-343 (2015)

[12] Gers, F.A., Schmidhuber, J., Cummins, F.A.: Learning to forget: Continual prediction with lstm. Neural Computation 12(10), 2451-2471 (2000) [OpenAIRE]

[13] Goldberg, Y., Nivre, J.: A dynamic oracle for arc-eager dependency parsing. In: COLING 2012, 24th International Conference on Computational Linguistics, Technical Papers, 8-15 December 2012, Mumbai, India, pp. 959-976 (2012)

[14] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735- 1780 (1997)

[15] Kiperwasser, E., Goldberg, Y.: Simple and accurate dependency parsing using bidirectional lstm feature representations. TACL 4, 313-327 (2016) [OpenAIRE]

37 references, page 1 of 3
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