The goal of dialogue topic shift detection is to identify whether the current
topic in a conversation has changed or needs to change. Previous work focused
on detecting topic shifts using pre-trained models to encode the utterance,
failing to delve into the various levels of topic granularity in the dialogue
and understand dialogue contents. To address the above issues, we take a
prompt-based approach to fully extract topic information from dialogues at
multiple-granularity, i.e., label, turn, and topic. Experimental results on our
annotated Chinese Natural Topic Dialogue dataset CNTD and the publicly
available English TIAGE dataset show that the proposed model outperforms the
baselines. Further experiments show that the information extracted at different
levels of granularity effectively helps the model comprehend the conversation
topics.

通过提示式方法从对话中多层次提取主题信息，可以有效地识别对话主题的变化与多样性，且实验结果显示该方法优于现有方法。

多粒度提示在对话中的话题转移检测

Multi-Granularity Prompts for Topic Shift Detection in Dialogue

Accurate prediction of conversation topics can be a valuable signal for
creating coherent and engaging dialog systems. In this work, we focus on
context-aware topic classification methods for identifying topics in free-form
human-chatbot dialogs. We extend previous work on neural topic classification
and unsupervised topic keyword detection by incorporating conversational
context and dialog act features. On annotated data, we show that incorporating
context and dialog acts leads to relative gains in topic classification
accuracy by 35% and on unsupervised keyword detection recall by 11% for
conversational interactions where topics frequently span multiple utterances.
We show that topical metrics such as topical depth is highly correlated with
dialog evaluation metrics such as coherence and engagement implying that
conversational topic models can predict user satisfaction. Our work for
detecting conversation topics and keywords can be used to guide chatbots
towards coherent dialog.

本文提出了一种上下文感知的谈话主题分类方法，通过引入对话上下文和对话行为特征，扩展了神经主题分类和无监督主题关键词检测的先前研究，以提高谈话中的主题识别准确性和预测用户评价指标。