Nowadays, customer support systems are one of the key factors in maintaining any big company's reputation and success. These systems are capable of handling a large number of tickets systemically and provides a mechanism to track/logs the communication between customer and support agents. Companies invest huge amounts of money in training support agents and deploying customer care services for their products and services. Support agents are responsible for handling different customer queries and implementing required actions to solve a particular issue or problem raised by the service/product user. In a bigger picture, customer support systems could receive a large amount of ticket raised depending upon the number of users and services being offered. Customer care service gets directly affected due to the high volume of tickets and a limited number of support agents. Therefore, providing support agents with the recommendations about the possible resolution actions for a new ticket would be helpful and can save a lot of time. This study is focused on the development of an end-to-end framework for suggesting resolution actions rather than recommending free form resolution text against a newly raised ticket. To develop such a system, the pipeline is broadly divided into four components that are data preprocessing, actions extractor, resolution predictor, and evaluation. In actions extractor module, we have proposed a technique to identify and extract actionable phrases from resolution text. For resolution predictor, we have proposed two different pipelines that are referred as “Similarity Search Model” and “End-to-End Model.” The similarity search method is based on a ticket similarity search to find the most relevant historical tickets which then leads to corresponding resolution actions. On the other hand, end-to-end model make use of actions extractor module directly and implemented in a way to directly predict resolution actions. To compare and evaluate the mentioned methods on the same ground, we also proposed an actions evaluation criterion which uses BertScore and METEOR score jointly to compute the score against actual and predicted actions for a particular test ticket. The analysis and experiments are performed on the real-world IBM ticket data set. Overall, we observed that end-to-end model outperformed similarity search-based methods and achieved better performance and scores comparatively. The trained models and code are available at https://bit.ly/2GbUBVk.

中文翻译：

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支持票证自动解决方案建议的多方法通用框架

如今，客户支持系统是维持任何大公司的声誉和成功的关键因素之一。这些系统能够系统地处理大量工单，并提供一种机制来跟踪/记录客户和支持代理之间的通信。公司在培训支持代理和为其产品和服务部署客户关怀服务方面投入了大量资金。支持代理负责处理不同的客户查询并实施所需的操作以解决服务/产品用户提出的特定问题或问题。从更大的角度来看，客户支持系统可能会收到大量的票，这取决于所提供的用户和服务的数量。由于工单数量众多且支持代理数量有限，客户服务直接受到影响。因此，向支持代理提供有关新工单可能的解决措施的建议会很有帮助，并且可以节省大量时间。本研究的重点是开发端到端框架，用于建议解决行动，而不是针对新提出的票证推荐自由格式的解决文本。为了开发这样一个系统，管道大致分为四个组件，即数据预处理、动作提取器、分辨率预测器和评估。在动作提取器模块中，我们提出了一种从解析文本中识别和提取可操作短语的技术。对于分辨率预测器，我们提出了两种不同的管道，称为“相似性搜索模型”和“端到端模型”。相似度搜索方法基于票证相似度搜索，以找到最相关的历史票证，然后导致相应的解决动作。另一方面，端到端模型直接使用动作提取器模块并以直接预测解决动作的方式实现。为了在相同的基础上比较和评估上述方法，我们还提出了一个动作评估标准，它使用 BertScore 和 METEOR 分数联合计算针对特定测试票的实际和预测动作的分数。分析和实验是在真实世界的 IBM 票据数据集上进行的。总体，我们观察到端到端模型优于基于相似性搜索的方法，并且相对而言取得了更好的性能和分数。训练有素的模型和代码可在 https://bit.ly/2GbUBVk 获得。