{"id":680343,"date":"2020-07-29T11:57:28","date_gmt":"2020-07-29T18:57:28","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=680343"},"modified":"2020-07-30T15:37:30","modified_gmt":"2020-07-30T22:37:30","slug":"recent-advances-in-conversational-information-retrieval","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/recent-advances-in-conversational-information-retrieval\/","title":{"rendered":"Recent Advances in Conversational Information Retrieval"},"content":{"rendered":"

Recent progress in deep learning has brought tremendous improvements in conversational AI, leading to a plethora of commercial conversational services that allow naturally spoken interactions, increasing the need for more human-centric interactions in IR. As a result, we have witnessed a resurgent interest in developing modern CIR systems in research communities and industry. This tutorial presents recent advances in CIR, focusing mainly on neural approaches and new applications developed in the past five years. Our goal is to provide a thorough and in-depth overview of the general definition of CIR, the components of CIR systems, new applications raised for its conversational aspects, and the (neural) techniques recently developed for it.<\/p>\n","protected":false},"excerpt":{"rendered":"

Recent progress in deep learning has brought tremendous improvements in conversational AI, leading to a plethora of commercial conversational services that allow naturally spoken interactions, increasing the need for more human-centric interactions in IR. As a result, we have witnessed a resurgent interest in developing modern CIR systems in research communities and industry. This tutorial 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