Large Language Models (LLMs) like ChatGPT have proven a great shallow understanding of many traditional NLP tasks, such as translation, summarization, etc. However, its performance on high-level understanding, such as dialogue discourse analysis task that requires a higher level of understanding and reasoning, remains less explored. This study investigates ChatGPT's capabilities in three dialogue discourse tasks: topic segmentation, discourse relation recognition, and discourse parsing, of varying difficulty levels. To adapt ChatGPT to these tasks, we propose discriminative and generative paradigms and introduce the Chain of Thought (COT) approach to improve ChatGPT's performance in more difficult tasks. The results show that our generative paradigm allows ChatGPT to achieve comparative performance in the topic segmentation task comparable to state-of-the-art methods but reveals room for improvement in the more complex tasks of discourse relation recognition and discourse parsing. Notably, the COT can significantly enhance ChatGPT's performance with the help of understanding complex structures in more challenging tasks. Through a series of case studies, our in-depth analysis suggests that ChatGPT can be a good annotator in topic segmentation but has difficulties understanding complex rhetorical structures. We hope these findings provide a foundation for future research to refine dialogue discourse analysis approaches in the era of LLMs.

翻译：像ChatGPT这样的大语言模型已被证明在许多传统自然语言处理任务（如翻译、摘要等）中具备良好的浅层理解能力。然而，其在需要更深层次理解与推理的对话语篇分析等高级理解任务中的表现，仍有待探索。本研究考察了ChatGPT在三种不同难度的对话语篇任务中的能力：话题分割、语篇关系识别和语篇解析。为使ChatGPT适应这些任务，我们提出了判别式与生成式两种范式，并引入思维链方法来提升ChatGPT在更困难任务上的表现。结果显示，我们的生成式范式使ChatGPT在话题分割任务上达到了与最先进方法相当的性能，但在更复杂的语篇关系识别和语篇解析任务中仍有改进空间。值得注意的是，思维链方法借助对复杂结构的理解，能显著提升ChatGPT在更具挑战性任务中的表现。通过一系列案例研究，我们的深度分析表明，ChatGPT可成为话题分割任务中的优秀标注工具，但在理解复杂修辞结构方面存在困难。我们希望这些发现能为未来在大语言模型时代改进对话语篇分析方法奠定基础。