A Corpus of Controlled Opinionated and Knowledgeable Movie Discussions for Training Neural Conversation Models (2020.lrec-1)
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| Challenge: | Fully data driven Chatbots suffer from inconsistent behaviour across their turns due to a general difficulty in controlling parameters like their assumed background personality and knowledge of facts. |
| Approach: | They propose a model that is based on pre-specified facts and opinions and validates the dialogues for adherence to their given fact and opinion profile. |
| Outcome: | The proposed model is able to generate opinionated responses that are judged to be natural and knowledgeable and show attentiveness. |
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Xiangru Tang, Arjun Nair, Borui Wang, Bingyao Wang, Jai Desai, Aaron Wade, Haoran Li, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev
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| Challenge: | Current neural network-based conversational models lack diversity and generate boring responses to open-ended utterances. |
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| Challenge: | TicketTalk dataset with 23,789 annotated dialogs is a data-driven, end-to-end approach to transaction-based dialog systems that performs at near-human levels in terms of verbal response quality and factual grounding accuracy. |
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PRODIGy: a PROfile-based DIalogue Generation dataset (2024.findings-naacl)
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| Challenge: | Existing profiles-based dialogue datasets lack explicit profile representations or are difficult to collect. |
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