Papers with large-scale conversation
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading (P19-1)
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Lianhui Qin, Michel Galley, Chris Brockett, Xiaodong Liu, Xiang Gao, Bill Dolan, Yejin Choi, Jianfeng Gao
| Challenge: | a new approach to contentful neural conversation is proposed . end-to-end models are effective in learning fluent responses, but their responses are often vacuous and uninformative. |
| Approach: | They propose a model that provides the conversation model with relevant text on the fly as a source of external knowledge. |
| Outcome: | The proposed model improves the informativeness and diversity of generated output compared to previous methods. |
From Chat Logs to Collective Insights: Aggregative Question Answering (2025.emnlp-main)
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| Challenge: | Existing approaches to analyzing large-scale conversation logs treat interactions as independent, missing critical insights. |
| Approach: | They propose a task that requires models to reason explicitly over thousands of user-chatbot interactions to answer aggregational queries. |
| Outcome: | The proposed task requires models to reason over thousands of user-chatbot interactions to answer aggregational queries such as identifying emerging concerns among demographics. |