Question Quality Improvement: Deep Question Understanding for Incident Management in Technical Support Domain - Ray, Anupama; Aggarwal, Pooja; Hadhazi, Csaba; Dasgupta, Gargi; Paradkar, Amit (Proceedings of the AAAI Conference on Artificial Intelligence, 2020)
Technical support domain involves solving problems from user queries through various channels: voice, web and chat, and is both time-consuming and labour intensive. The textual queries in web or chat mode are unstructured and often incomplete. This affects information retrieval and increases the difficulty level for agents to solve it. Such cases require multiple rounds of interaction between user and agent/chatbot in order to better understand the user query. This paper presents a deployed system called Question Quality Improvement (QQI), that aims to improve the quality of user utterance by understanding and extracting important parts of an utterance and gamifying the user interface, prompting them to enter the remaining relevant information. QQI is guided by an ontology designed for the technical support domain and uses co-reference resolution and deep parsing to understand the sentences. Using the syntactics and semantics in the deep parse tree structure various attributes in the ontology are extracted. The system has been in production for over two years supporting around 800 products resulting in a reduction in the time-to-resolve cases by around 29%, leading to huge cost savings. QQI being a core natural language understanding and metadata extraction technology, directly affects more than 8K tickets everyday. These cases are submitted after 50K edits done on the case based on QQI feedback. QQI outputs are used by other technologies such as search and retrieval, case routing for automated dispatch, case-difficulty-prediction, and by the chatbots supported in each product page.