%0 Generic %1 gao2018neural %A Gao, Jianfeng %A Galley, Michel %A Li, Lihong %D 2018 %K masterthesis qna survey %T Neural Approaches to Conversational AI %U http://arxiv.org/abs/1809.08267 %X The present paper surveys neural approaches to conversational AI that have been developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) chatbots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between them and traditional approaches, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies.

@misc{gao2018neural, abstract = {The present paper surveys neural approaches to conversational AI that have been developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) chatbots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between them and traditional approaches, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies.}, added-at = {2020-12-04T09:32:14.000+0100}, author = {Gao, Jianfeng and Galley, Michel and Li, Lihong}, biburl = {https://www.bibsonomy.org/bibtex/2d40fcaca24cc1b6b5cc9a198e89f20d7/festplatte}, description = {[1809.08267] Neural Approaches to Conversational AI}, interhash = {1431e167838c6794693a35e0a77590d1}, intrahash = {d40fcaca24cc1b6b5cc9a198e89f20d7}, keywords = {masterthesis qna survey}, note = {cite arxiv:1809.08267Comment: Foundations and Trends in Information Retrieval (95 pages)}, timestamp = {2020-12-04T09:32:14.000+0100}, title = {Neural Approaches to Conversational AI}, url = {http://arxiv.org/abs/1809.08267}, year = 2018 }