Human-like informative conversations: Better acknowledgements using conditional mutual information (2021.naacl-main)
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| Challenge: | Existing chatbots generate responses that are non-specific w.r.t. one of the contexts, typically the conversational history. |
| Approach: | They propose to build a dialogue agent that can weave new factual content into conversations as naturally as humans. |
| Outcome: | The proposed method trades off pmi for pcmi_h and is preferred by humans for overall quality over the Max-PMI baseline 60% of the time. |
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| Challenge: | Xu et al., 2023) and Bai ed., 2019) use crowdworkers to collect signals from natural dialogue episodes. |
| Approach: | They use the publicly released BlenderBot deployment data to extract signals from conversations to implicitly measure the quality of a machine-generated utterance. |
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Language Models in Dialogue: Conversational Maxims for Human-AI Interactions (2024.findings-emnlp)
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Erik Miehling, Manish Nagireddy, Prasanna Sattigeri, Elizabeth Daly, David Piorkowski, John Richards
| Challenge: | Modern language models exhibit some inherent shortcomings, particularly in conversational settings. |
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You Impress Me: Dialogue Generation via Mutual Persona Perception (2020.acl-main)
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| Challenge: | Existing chit-chat systems tend to generate uninformative responses and lack coherent personality traits due to the diversity of speakers. |
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Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations (2024.acl-long)
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| Challenge: | Existing models for language from a social perspective are gaining popularity . we present a generalizable classification approach that leverages Large Language Models . |
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Ditch the Gold Standard: Re-evaluating Conversational Question Answering (2022.acl-long)
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| Challenge: | Existing conversational question answering systems provide natural-language answers to users in information-seeking conversations. |
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| Challenge: | a recent rise in polarization has led to a rise in the use of loud and extreme voices in public spaces. |
| Approach: | They propose ways to parse and convey information from small-group recorded conversations . they show that LLMs can provide socially relevant context to improve comprehension . |
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Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances (2021.acl-long)
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| Challenge: | Recent intelligent open-domain chatbots have made substantial progress thanks to the rapid development of large-scale pre-training approaches. |
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What makes a good conversation? How controllable attributes affect human judgments (N19-1)
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| Challenge: | Existing work on dialogue models for conversational quality is incompletely understanding the relationship between quality and individual attributes. |
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Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity (D18-1)
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| Challenge: | Existing encoder-decoder models for open domain dialogue generate generic, uninformative, and non-coherent responses. |
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Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations (2025.findings-naacl)
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Hao Yang, Hongyuan Lu, Xinhua Zeng, Yang Liu, Xiang Zhang, Haoran Yang, Yumeng Zhang, Shan Huang, Yiran Wei, Wai Lam
| Challenge: | a new paradigm for dialogue systems is being developed to mimic human interactions . the current single-step dialogue paradigm lacks the depth and fluidity of human interactions. |
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