Two-pass Discourse Segmentation with Pairing and Global Features

Code

• github.com/ZiningZhu/feng-hirst-rst-parser
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• github.com/arne-cl/feng-hirst-rst-parser
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• github.com/NLPbox/feng-hirst-service
  none★ 0
• github.com/Akanni96/feng-hirst-rst-parser
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Abstract

Previous attempts at RST-style discourse segmentation typically adopt features centered on a single token to predict whether to insert a boundary before that token. In contrast, we develop a discourse segmenter utilizing a set of pairing features, which are centered on a pair of adjacent tokens in the sentence, by equally taking into account the information from both tokens. Moreover, we propose a novel set of global features, which encode characteristics of the segmentation as a whole, once we have an initial segmentation. We show that both the pairing and global features are useful on their own, and their combination achieved an F_1 of 92.6% of identifying in-sentence discourse boundaries, which is a 17.8% error-rate reduction over the state-of-the-art performance, approaching 95% of human performance. In addition, similar improvement is observed across different classification frameworks.

Tasks

Reproductions