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.

该研究提出了一种利用主题分割技术进行远程监督的方法来解决现代话语解析中由于数据稀缺问题所面临的困境，实验结果表明该方法能够有效地产生准确的树状结构并较之前的方法取得了更高的得分。