Audio call transcripts are one of the valuable sources of information for multiple downstream use cases such as understanding the voice of the customer and analyzing agent performance. However, these transcripts are noisy in nature and in an industry setting, getting tagged ground truth data is a challenge. In this paper, we present a solution implemented in the industry using BERT Language Models as part of our pipeline to extract key topics and multiple open intents discussed in the call. Another problem statement we looked at was the automatic tagging of transcripts into predefined categories, which traditionally is solved using supervised approach. To overcome the lack of tagged data, all our proposed approaches use unsupervised methods to solve the outlined problems. We evaluate the results by quantitatively comparing the automatically extracted topics, intents and tagged categories with human tagged ground truth and by qualitatively measuring the valuable concepts and intents that are not present in the ground truth. We achieved near human accuracy in extraction of these topics and intents using our novel approach

翻译：听觉呼叫记录誊本是多个下游使用案例的宝贵信息来源之一,例如了解客户的声音和分析代理人的绩效。然而,这些记录誊本在性质上和行业环境中都很吵闹,获得贴标签的地面真相数据是一项挑战。在本文中,我们介绍了在行业中采用BERT语言模型作为我们管道的一部分来提取关键主题和呼吁中讨论的多种公开意图的解决方案。我们研究的另一个问题说明是,将记录誊本自动标记成预先界定的类别,传统上通过监督方法予以解决。为了克服缺乏贴标签数据的问题,我们提出的所有方法都使用未经监督的方法来解决概述的问题。我们通过将自动提取的专题、意图和标记类别与人类贴标签的地面真相进行定量比较,并通过从质量上衡量在地面真相中不存在的宝贵概念和意图,对结果进行评估。我们利用我们的新方法,在提取这些专题和意图方面几乎达到了人类的准确度。