Data Collection for Empirically Determining the Necessary Information for Smooth Handover in Dialogue (2022.lrec-1)
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| Challenge: | Despite advances in deep learning, dialogue systems struggle to achieve fully autonomous transactions with users. |
| Approach: | They conducted an experiment in which two operators switched periodically while performing chat, consultation, and sales tasks in dialogue. |
| Outcome: | The results show that adjacency pairs are useful for recording conversation history . key-value pairs are also useful when there are underlying tasks, such as consultation and sales . |
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Data Collection and End-to-End Learning for Conversational AI (D19-2)
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| Challenge: | tutorial aims to familiarise research community with recent advances in statistical dialogue systems . focus of tutorial is on learning end-to-end from data and their relation to more common modular systems. |
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| Challenge: | Existing knowledge grounding models focus on locating knowledge in document contexts that are relevant to the conversation. |
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Jianguo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong
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