面向任务型对话的意图归纳中话语嵌入与聚类方法分析
Analysis of Utterance Embeddings and Clustering Methods Related to Intent Induction for Task-Oriented Dialogue
AI总结：
本文研究任务型对话中意图归纳的无监督方法，比较不同话语嵌入与聚类算法的组合，发现预训练MiniLM结合Agglomerative聚类在NMI、ARI、F1、准确率和覆盖率上显著提升。
摘要：
本工作的重点是研究无监督方法，以克服设计任务型对话模式中的典型挑战：为每个对话轮次分配意图标签（意图聚类）以及基于意图聚类方法生成一组意图（意图归纳）。我们假设意图的自动归纳存在两个显著因素：（1）用于意图标注的聚类算法和（2）用户话语嵌入空间。我们基于DSTC11评估比较了现有的现成聚类模型和嵌入。大量实验表明，在意图归纳任务中，应仔细考虑话语嵌入与聚类方法的组合选择。我们还展示了预训练的MiniLM与Agglomerative聚类相结合，在意图归纳任务中的NMI、ARI、F1、准确率和示例覆盖率方面均有显著提升。源代码可在https://github.com/Jeiyoon/dstc11-track2获取。
英文摘要：
The focus of this work is to investigate unsupervised approaches to overcome quintessential challenges in designing task-oriented dialog schema: assigning intent labels to each dialog turn (intent clustering) and generating a set of intents based on the intent clustering methods (intent induction). We postulate there are two salient factors for automatic induction of intents: (1) clustering algorithm for intent labeling and (2) user utterance embedding space. We compare existing off-the-shelf clustering models and embeddings based on DSTC11 evaluation. Our extensive experiments demonstrate that the combined selection of utterance embedding and clustering method in the intent induction task should be carefully considered. We also present that pretrained MiniLM with Agglomerative clustering shows significant improvement in NMI, ARI, F1, accuracy and example coverage in intent induction tasks. The source codes are available at https://github.com/Jeiyoon/dstc11-track2.
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。
