Agent Assist through Conversation Analysis (2020)

Abstract: Customer support agents play a crucial role as an interface between an organization and its end-users. We propose CAIRAA: Conversational Approach to Information Retrieval for Agent Assistance, to reduce the cognitive workload of support agents who engage with users through conversation systems. CAIRAA monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. We combine traditional information retrieval (IR) approaches with more recent Deep Learning (DL) models to ensure high accuracy and efficient run-time performance in the deployed system. Here, we describe the CAIRAA system and demonstrate its effectiveness in a pilot study via a short video.

## introduction customer care conversation systems have been used in a variety of domains, including technical support, reservation systems, and banking applications @xcite . the majority of such systems provide a dashboard to customer support agents, so that they can interact with multiple end-users in parallel. the support agents use such dashboards to perform diverse tasks to address user requests, such as question-answering, conversational search, document passage extraction and transactions. when identifying the responses to user queries, the support agents either, (1) rely on their own domain knowledge and articulate such knowledge to be sent to the users or, (2) manually extract the keywords from the conversation and use search functions provided in their dashboards to identify the relevant knowledge contained in documents, and send urls for these documents. the continuous process of identifying knowledge and responding becomes an immense cognitive workload for the customer support agents. although automated conversation systems have improved immensely in the last decade with advances in natural language processing, machine learning and dialog management @xcite al. (2016); li et al. (2016); li et al. (2017) ), these systems still fail to satisfy the sophisticated customer's needs in real-life scenarios. this leads to frustration @xcite and less engagement @xcite . therefore, having an empathetic human agent in-the-loop supported by efficient and accurate content retrieval, allows better coverage of customer needs and reduces customer frustration. with cairaa, we propose and showcase a system that provides real-time assistance to the support agents and alleviates their cognitive workload. our system provides two forms of real-time recommendations to the support agents: (1) urls of the documents that contain information to resolve user issues, and (2) natural language responses that the agent can use to respond to their customer's queries. the operation of cairaa is illustrated in ## system the architecture of cairaa is illustrated in figure 1 . it consists of an agent-dashboard that is used by customer-support agents to interact with customers (users). the agent is assisted by recommendations from two engines -document recommendation engine and the response recommendation engine. as their names suggest, these engines provide realtime recommendations for documents that could be relevant during the chat, as well as, responses to the agent. figure 1 also depicts components for data storage as well as model training. ## https://github.com/spotify/annoy deployment details once the recommendation engines are trained, cairaa is tasked with the live operation of the agent dashboard. cairaa adopts optimized user interface design to deliver precise information with minimal agent interactions. prioritizing scalability, each operational component is intentionally designed to be modular and stateless. ## conclusion and future work by providing real-time assistance to agents to support their clients, we leverage the speed and accuracy of automated recommendation engines while retaining the agents' expertise. learning from conversation logs, cairaa promotes more uniform support by keeping all agents aware of the latest information to address current end-user needs. combining traditional information retrieval approaches with modern deep learning models ensures high accuracy and efficient run-time performance in our deployed system.