Abstract
Discourse parsing aims to identify structures and relationships between different discourse units. Most existing approaches analyze a whole discourse at once, which often fails in distinguishing long-span relations and properly representing discourse units. In this article, we propose a novel parsing model to analyze discourse in a two-step fashion with different feature representations to characterize intra sentence and inter sentence discourse structures, respectively. Our model works in a transition-based framework and benefits from a stack long short-term memory neural network model. Experiments on benchmark tree banks show that our method outperforms traditional 1-step parsing methods in both English and Chinese.
- Miguel Ballesteros, Chris Dyer, and Noah A. Smith. 2015. Improved transition-based parsing by modeling characters instead of words with LSTMs. In EMNLP’15, Lisbon, Portugal. 349--359.Google Scholar
- Or Biran and Kathleen McKeown. 2013. Aggregated word pair features for implicit discourse relation disambiguation. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL’13), 4-9 August 2013, Sofia, Bulgaria, Volume 2: Short Papers. 69--73. http://aclweb.org/anthology/P/P13/P13-2013.pdf.Google Scholar
- Chloé Braud, Maximin Coavoux, and Anders Søgaard. 2017. Cross-lingual RST discourse parsing. CoRR abs/1701.02946 (2017). http://arxiv.org/abs/1701.02946Google Scholar
- Lynn Carlson, Daniel Marcu, and Mary Ellen Okurovsky. 2001. Building a discourse-tagged corpus in the framework of rhetorical structure theory. In Proceedings of the SIGDIAL’01 Workshop, The 2nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, Saturday, September 1, 2001 to Sunday, September 2, 2001, Aalborg, Denmark. http://aclweb.org/anthology/W/W01/W01-1605.pdf. Google Scholar
Digital Library
- Danqi Chen and Christopher D. Manning. 2014. A fast and accurate dependency parser using neural networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14), October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL. 740--750. http://aclweb.org/anthology/D/D14/D14-1082.pdf.Google Scholar
- Jifan Chen, Qi Zhang, Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016. Implicit discourse relation detection via a deep architecture with gated relevance network. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL’16), August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers. http://aclweb.org/anthology/P/P16/P16-1163.pdf.Google Scholar
Cross Ref
- Michael Collins and Brian Roark. 2004. Incremental parsing with the perceptron algorithm. In ACL’04, 21-26 July, 2004, Spain. 111--118. Google Scholar
Digital Library
- Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, and Noah A. Smith. 2015. Transition-based dependency parsing with stack long short-term memory. In ACL’15, Volume 1. 334--343.Google Scholar
- Vanessa Wei Feng and Graeme Hirst. 2012. Text-level discourse parsing with rich linguistic features. In ACL’12, July 8-14, 2012, Jeju Island, Korea - Volume 1: Long Papers. 60--68. http://www.aclweb.org/anthology/P12-1007. Google Scholar
Digital Library
- Vanessa Wei Feng and Graeme Hirst. 2014. A linear-time bottom-up discourse parser with constraints and post-editing. In ACL’14, Baltimore, MD, USA, Volume 1. 511--521.Google Scholar
- David A. Ferrucci, Eric W. Brown, Jennifer Chu-Carroll, James Fan, David Gondek, Aditya Kalyanpur, Adam Lally, J. William Murdock, Eric Nyberg, John M. Prager, Nico Schlaefer, and Christopher A. Welty. 2010. Building Watson: An overview of the deepQA project. AI Magazine 31, 3, 59--79.Google Scholar
Digital Library
- Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier neural networks. In AISTATS’11, Fort Lauderdale, USA, April 11-13, 2011.Google Scholar
- Udo Hahn. 2002. The theory and practice of discourse parsing and summarization by Daniel Marcu. Computational Linguistics 28, 1, 81--83. Google Scholar
Digital Library
- Hugo Hernault, Helmut Prendinger, David A. duVerle, and Mitsuru Ishizuka. 2010. HILDA: A discourse parser using support vector machine classification. D8D 1, 3, 1--33.Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8, 1735--1780. Google Scholar
Digital Library
- Yangfeng Ji, Gongbo Zhang, and Jacob Eisenstein. 2015. Closing the gap: Domain adaptation from explicit to implicit discourse relations. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP’15), Lisbon, Portugal, September 17-21, 2015. 2219--2224. http://aclweb.org/anthology/D/D15/D15-1264.pdf.Google Scholar
Cross Ref
- Yanyan Jia, Yansong Feng, Bingfeng Luo, Yuan Ye, Tianyang Liu, and Dongyan Zhao. 2016. Transition-based discourse parsing with multilayer stack long short term memory. In Proceedings of the Natural Language Understanding and Intelligent Applications - 5th CCF Conference on Natural Language Processing and Chinese Computing (NLPCC’16), and 24th International Conference on Computer Processing of Oriental Languages (ICCPOL’16), Kunming, China, December 2-6, 2016.360--373.Google Scholar
Cross Ref
- Shafiq R. Joty, Giuseppe Carenini, Raymond T. Ng, and Yashar Mehdad. 2013. Combining intra- and multi-sentential rhetorical parsing for document-level discourse analysis. In ACL’13. 486--496.Google Scholar
- Shafiq R. Joty and Alessandro Moschitti. 2014. Discriminative reranking of discourse parses using tree kernels. In EMNLP’14, October 25-29, 2014, Doha, Qatar. 2049--2060. http://aclweb.org/anthology/D/D14/D14-1219.pdf.Google Scholar
- Huong LeThanh. 2004. Generating discourse structures for written texts. Proceedings of the 20th International Conference on Computational Linguistics. Google Scholar
Digital Library
- Jiwei Li, Rumeng Li, and Eduard H. Hovy. 2014. Recursive deep models for discourse parsing. In EMNLP’14, October 25-29, 2014. 2061--2069. http://aclweb.org/anthology/D/D14/D14-1220.pdf.Google Scholar
- Sujian Li, Liang Wang, Ziqiang Cao, and Wenjie Li. 2014. Text-level discourse dependency parsing. In ACL’14, Baltimore, MD, Volume 1. 25--35.Google Scholar
- Yancui Li, Jing Sun, Fang Kong, and Guodong Zhou. 2014. Building Chinese discourse corpus with connective-driven dependency tree structure. In EMNLP’14. http://www.aclweb.org/anthology/D/D14/D14-1224.pdf.Google Scholar
- Ziheng Lin, Min-Yen Kan, and Hwee Tou Ng. 2009. Recognizing implicit discourse relations in the Penn discourse treebank. In EMNLP’09, 6-7 August 2009, Singapore. 343--351. http://www.aclweb.org/anthology/D09-1036. Google Scholar
Digital Library
- Yang Liu and Sujian Li. 2016. Recognizing implicit discourse relations via repeated reading: Neural networks with multi-level attention. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP’16), Austin, Texas, USA, November 1-4, 2016. 1224--1233. http://aclweb.org/anthology/D/D16/D16-1130.pdf.Google Scholar
Cross Ref
- D. L. Long, C. L. Johns, and E. Jonathan. 2012. A memory-retrieval view of discourse representation: The recollection and familiarity of text ideas. Language and Cognitive Processes 27, 6, 821--843.Google Scholar
Cross Ref
- Annie Louis, Aravind K. Joshi, and Ani Nenkova. Discourse indicators for content selection in summarization. In SIGDIAL’10. Google Scholar
Digital Library
- G. McKoon and R. Ratcliff. 1998. Memory-based language processing: Psycholinguistic research in the 1990s. Annual Review of Psychology, vol. 49, 25–42.Google Scholar
Cross Ref
- Jane Morris and Graeme Hirst. 1991. Lexical cohesion computed by thesaural relations as an indicator of the structure of text. Computational Linguistics 17, 1, 21--48. Google Scholar
Digital Library
- Joakim Nivre. 2009. Non-projective dependency parsing in expected linear time. In ACL’09. http://www.aclweb.org/anthology/P09-1040. Google Scholar
Digital Library
- Joakim Nivre and Mario Scholz. 2004. Deterministic dependency parsing of English text. In COLING 2’04, 23-27 August 2004, Geneva, Switzerland. Google Scholar
Digital Library
- Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14), October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, A Special Interest Group of the ACL. 1532--1543. http://aclweb.org/anthology/D/D14/D14-1162.pdf.Google Scholar
- Emily Pitler, Annie Louis, and Ani Nenkova. 2009. Automatic sense prediction for implicit discourse relations in text. In ACL’09, Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 2-7 August 2009, Singapore. 683--691. http://www.aclweb.org/anthology/P09-1077. Google Scholar
Digital Library
- Emily Pitler, Mridhula Raghupathy, Hena Mehta, Ani Nenkova, Alan Lee, and Aravind K. Joshi. 2008. Easily identifiable discourse relations. In COLING’08, 22nd International Conference on Computational Linguistics, Posters Proceedings, 18-22 August 2008, Manchester, UK. 87--90. http://www.aclweb.org/anthology/C08-2022Google Scholar
- Richard Socher, Andrej Karpathy, Quoc V. Le, Christopher D. Manning, and Andrew Y. Ng. 2014. Grounded compositional semantics for finding and describing images with sentences. TACL 2, 207--218.Google Scholar
Cross Ref
- Kimberly D. Voll and Maite Taboada. 2007. Not all words are created equal: Extracting semantic orientation as a function of adjective relevance. In Proceedings of AI’07, Gold Coast, Australia, December 2-6, 2007. 337--346. Google Scholar
Digital Library
- Yuping Zhou and Nianwen Xue. 2012. PDTB-style discourse annotation of Chinese text. In the 50th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, July 8-14, 2012, Jeju Island, Korea - Volume 1: Long Papers. 69--77. http://www.aclweb.org/anthology/P12-1008. Google Scholar
Digital Library
- R. A. Zwaan and Gabriel A. Radvansky. 1998. Situation models in language comprehension and memory. Sychological Bulletin 123, 2.Google Scholar
Cross Ref
Index Terms
Improved Discourse Parsing with Two-Step Neural Transition-Based Model
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