迈向客户支持对话中意图归纳的完全无监督框架
Towards a Fully Unsupervised Framework for Intent Induction in Customer Support Dialogues
AI总结：
本文提出一种完全无监督的对话意图归纳框架，通过预处理语料库提升效果并提取意图对话流，在MultiWOZ数据集上验证，适用于真实客户支持场景。
摘要：
目前最先进的意图归纳模型需要带标注的数据集。然而，标注对话耗时、费力且昂贵。在本研究中，我们提出了一种在对话内进行意图归纳的完全无监督框架。此外，我们展示了预处理对话语料库如何改善结果。最后，我们展示了如何通过研究最常见序列来提取意图的对话流。尽管我们在MultiWOZ数据集上测试了本研究，但该框架无需先验知识，使其适用于任何可能的用例，这使其与工业界跨领域的真实世界客户支持应用高度相关。
英文摘要：
State of the art models in intent induction require annotated datasets. However, annotating dialogues is time-consuming, laborious and expensive. In this work, we propose a completely unsupervised framework for intent induction within a dialogue. In addition, we show how pre-processing the dialogue corpora can improve results. Finally, we show how to extract the dialogue flows of intentions by investigating the most common sequences. Although we test our work in the MultiWOZ dataset, the fact that this framework requires no prior knowledge make it applicable to any possible use case, making it very relevant to real world customer support applications across industry.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
