TaskDiff：一种面向任务对话的相似度度量方法
TaskDiff: A Similarity Metric for Task-Oriented Conversations
AI总结：
针对现有文本相似度度量方法无法有效处理面向任务对话的问题，提出TaskDiff度量方法，利用话语、意图和槽位等对话组件及其分布计算相似度，在基准数据集上表现出更优性能与鲁棒性。
摘要：
对话式数字助手的普及使得大量对话数据得以获取，这些数据可用于改善用户体验和生成个性化响应。使用ChatGPT等流行的大型语言模型构建这些助手，还需要额外强调提示工程和评估方法。文本相似度度量是此类分析和评估的关键要素。尽管文献中已提出许多相似度度量方法，但它们对面向任务的对话并不有效，因为它们没有利用独特的对话特征。为了解决这一差距，我们提出了TaskDiff，这是一种新颖的对话相似度度量方法，它利用不同的对话组件（话语、意图和槽位）及其分布来计算相似度。在基准数据集上对TaskDiff进行的广泛实验评估表明，与其他相关方法相比，它具有优越的性能和更好的鲁棒性。
英文摘要：
The popularity of conversational digital assistants has resulted in the availability of large amounts of conversational data which can be utilized for improved user experience and personalized response generation. Building these assistants using popular large language models like ChatGPT also require additional emphasis on prompt engineering and evaluation methods. Textual similarity metrics are a key ingredient for such analysis and evaluations. While many similarity metrics have been proposed in the literature, they have not proven effective for task-oriented conversations as they do not take advantage of unique conversational features. To address this gap, we present TaskDiff, a novel conversational similarity metric that utilizes different dialogue components (utterances, intents, and slots) and their distributions to compute similarity. Extensive experimental evaluation of TaskDiff on a benchmark dataset demonstrates its superior performance and improved robustness over other related approaches.
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。
