Hierarchical Macro Discourse Parsing Based on Topic Segmentation

Hierarchically constructing micro (i. e. , intra-sentence or inter-sentence) discourse structure trees using explicit boundaries (e. g. , sentence and paragraph boundaries) has been proved to be an effective strategy. However, it is difficult to apply this strategy to document-level macro (i. e. , interparagraph) discourse parsing, the more challenging task, due to the lack of explicit boundaries at the higher level. To alleviate this issue, we introduce a topic segmentation mechanism to detect implicit topic boundaries and then help the document-level macro discourse parser to construct better discourse trees hierarchically. In particular, our parser first splits a document into several sections using the topic boundaries that the topic segmentation detects. Then it builds a smaller and more accurate discourse sub-tree in each section and sequentially forms a whole tree for a document. The experimental results on both Chinese MCDTB and English RST-DT show that our proposed method outperforms the state-of-the-art baselines significantly.

Authors

• Feng Jiang The Chinese University of Hong Kong, Shenzhen Shenzhen Research Institute of Big Data University of Science and Technology of China
• Yaxin Fan
• Xiaomin Chu
• Peifeng Li Natural Language Processing Lab, Soochow University, Suzhou, China
• Qiaoming Zhu
• Fang Kong Shanghai Jiao Tong University

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