Question Answering for Technical Customer Support

Human agents in technical customer support provide users with instructional answers to solve a task. Developing a technical support question answering (QA) system is challenging due to the broad variety of user intents. Moreover, user questions are noisy (for example, spelling mistakes), redundant and have various natural language expresses, which are challenges for QA system to match user queries to corresponding standard QA pair. In this work, we combine question intent categories classification and semantic matching model to filter and select correct answers from a back-end knowledge base. Using a real world user chatlog dataset with 60 intent categories, we observe that while supervised models, perform well on the individual classification tasks. For semantic matching, we add muti-info (answer and product information) into standard question and emphasize context information of user query (captured by GRU) into our model. Experiment results indicate that neural multi-perspective sentence similarity networks outperform baseline models. The precision of semantic matching model is 85%.