Question Answering for Technical Customer Support
(0) 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%.
Question and Answer Answer selection Semantic matching
Yang Li Qingliang Miao Ji Geng Christoph Alt Robert Schwarzenberg Leonhard Hennig Changjian Hu Feiyu Xu
Lenovo,Building H,No.6,West Shangdi Road,Haidian District Beijing,China DFKI,Alt-Moabit 91c,10559 Berlin,Germany
国际会议
2018自然语言处理与中文计算国际会议(NLPCC2018)
呼和浩特
英文
3-15
2018-08-26（万方平台首次上网日期，不代表论文的发表时间）