A NeuRetrieval Model for Human-Computer Conversations

Rui Yan, Dongyan Zhao
2018 Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18  
To establish an automatic conversation system between human and computer is regarded as one of the most hardcore problems in computer science. It requires interdisciplinary techniques of information retrieval, natural language processing, data management as well as artificial intelligence. The arrival of big data era reveals the feasibility to create a conversation system empowered by data-driven approaches. Now we are able to collect extremely large conversational data on Web, and organize
more » ... to launch a human-computer conversation system. Owing to the diversity of Web resources available, a retrieval-based conversation system will be able to find at least some responses from the massive data repository for any user inputs. Given a human issued utterance, i.e., a query, a retrieval-based conversation system will search for appropriate replies, conduct a relevance ranking, and then output the highly relevant one as the response. In this paper, we propose a novel retrieval model named NeuRetrieval for short text understanding, representation and semantic matching. The proposed model is general and unified for both single-turn and multi-turn conversation scenarios in open domain. In the experiments, we investigate the effectiveness of the proposed deep neural network model for human-computer conversations. We demonstrate performance improvement against a series of baseline methods in several evaluation metrics. In contrast with previously proposed methods, NeuRetrieval is tailored for conversation scenarios and demonstrated to be more effective.
doi:10.1145/3184558.3186341 dblp:conf/www/YanZ18 fatcat:zkrrazyuavfsnkrx577njf5bku