{"id":836284,"date":"2022-04-18T20:36:53","date_gmt":"2022-04-19T03:36:53","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=836284"},"modified":"2022-04-18T20:36:53","modified_gmt":"2022-04-19T03:36:53","slug":"a-survey-on-response-selection-for-retrieval-based-dialogues","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-survey-on-response-selection-for-retrieval-based-dialogues\/","title":{"rendered":"A Survey on Response Selection for Retrieval-based Dialogues"},"content":{"rendered":"
Building an intelligent dialogue system capable of naturally and coherently conversing with humans has been a long-standing goal of artificial intelligence. In the past decade, with the development of machine\/deep learning technology and the explosive growth of available conversation data in social media, numerous neural models have been developed for context-response matching tasks in retrieval-based 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