Predicting Above-Sentence Discourse Structure Using Distant Supervision from Topic Segmentation


  • Patrick Huber University of British Columbia
  • Linzi Xing University of British Columbia
  • Giuseppe Carenini University of British Columbia



Speech & Natural Language Processing (SNLP)


RST-style discourse parsing plays a vital role in many NLP tasks, revealing the underlying semantic/pragmatic structure of potentially complex and diverse documents. Despite its importance, one of the most prevailing limitations in modern day discourse parsing is the lack of large-scale datasets. To overcome the data sparsity issue, distantly supervised approaches from tasks like sentiment analysis and summarization have been recently proposed. Here, we extend this line of research by exploiting distant supervision from topic segmentation, which can arguably provide a strong and oftentimes complementary signal for high-level discourse structures. Experiments on two human-annotated discourse treebanks confirm that our proposal generates accurate tree structures on sentence and paragraph level, consistently outperforming previous distantly supervised models on the sentence-to-document task and occasionally reaching even higher scores on the sentence-to-paragraph level.




How to Cite

Huber, P., Xing, L., & Carenini, G. (2022). Predicting Above-Sentence Discourse Structure Using Distant Supervision from Topic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10), 10794-10802.



AAAI Technical Track on Speech and Natural Language Processing