GPT-通话：通过生成大型语言模型的合成对话，增强通话分割和标记

摘要

电话录音的转录在销售、客户服务、医疗保健和执法等各个领域具有重要价值。然而，对这些录音对话的分析可能是一个费时费力的过程，特别是在处理延续或多方面对话时。在这项工作中，我们提出了一种新颖的方法，即GPT蒸馏通话分割和标记（GPT-Calls），用于高效准确地进行通话分割和主题提取。GPT-Calls由离线和在线阶段组成。离线阶段仅应用于给定主题列表一次，涉及使用GPT模型为每个主题生成一组合成句子的分布并提取锚定向量。在线阶段应用于每通电话，评分转录对话与离线阶段找到的主题锚定之间的相似性。然后，对相似性评分进行时间域分析，将话语分组为段落并用主题标记。所提出的范式提供了一种准确高效的通话分割和主题提取方法，无需标记数据，因此是一种适用于各个领域的通用方法。我们的算法在Dynamics 365销售对话智能生产环境中运行，我们的研究基于从各个Dynamics 365销售租户收集的真实销售对话。

English

Transcriptions of phone calls are of significant value across diverse fields, such as sales, customer service, healthcare, and law enforcement. Nevertheless, the analysis of these recorded conversations can be an arduous and time-intensive process, especially when dealing with extended or multifaceted dialogues. In this work, we propose a novel method, GPT-distilled Calls Segmentation and Tagging (GPT-Calls), for efficient and accurate call segmentation and topic extraction. GPT-Calls is composed of offline and online phases. The offline phase is applied once to a given list of topics and involves generating a distribution of synthetic sentences for each topic using a GPT model and extracting anchor vectors. The online phase is applied to every call separately and scores the similarity between the transcripted conversation and the topic anchors found in the offline phase. Then, time domain analysis is applied to the similarity scores to group utterances into segments and tag them with topics. The proposed paradigm provides an accurate and efficient method for call segmentation and topic extraction that does not require labeled data, thus making it a versatile approach applicable to various domains. Our algorithm operates in production under Dynamics 365 Sales Conversation Intelligence, and our research is based on real sales conversations gathered from various Dynamics 365 Sales tenants.

GPT-通话：通过生成大型语言模型的合成对话，增强通话分割和标记

GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language Models

摘要

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