Abstract
Discourse structure has a central role in several NLP tasks, such as document translation, text summarization and dialogue generation. Also, text-level discourse parsing is notoriously difficult for the long distance of discourse and deep structures of discourse trees. In this paper, we build a tree-structured neural network for RST discourse parsing. We also introduce two tracking LSTMs to store long-distance information of a document to strengthen the representations for sentences and the entire document. Experimental results show that our proposed method obtains comparable performance regarding standard discourse parsing evaluations when compared with state-of-the-art systems.
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Notes
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The Penn Discourse TreeBank 2.0. https://catalog.ldc.upenn.edu/LDC2008T05.
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RMSprop is an unpublished, adaptive learning rate method proposed by Geoff Hinton.
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Acknowledgements
The authors would like to thank three anonymous reviewers for their comments on this paper. This research was supported by the General Program of the National Natural Science Foundation of China under Grant No. 61472264, the Artificial Intelligence Emergency Project under Grant No. 61751206, the sub-topic of the National Key Research and Development Program under Grant No. 2017YFB1002101, and the Young Scientists Fund of the National Natural Science Foundation of China under Grant No. 61502149.
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Zhang, L., Sun, C., Tan, X., Kong, F. (2019). RST Discourse Parsing with Tree-Structured Neural Networks. In: Chen, J., Zhang, J. (eds) Machine Translation. CWMT 2018. Communications in Computer and Information Science, vol 954. Springer, Singapore. https://doi.org/10.1007/978-981-13-3083-4_2
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DOI: https://doi.org/10.1007/978-981-13-3083-4_2
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