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. |
| Approach: | They propose a transmitter-receiver framework which explicitly models understanding between interlocutors. |
| Outcome: | The proposed framework improves on a large public dataset, Persona-Chat, with a significant boost over the state-of-the-art frameworks. |
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| Challenge: | Existing models for improving consistency often train with additional NLI labels or attach trained extra modules to the generative agent. |
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| Challenge: | Existing open-domain dialogue models fail to capture and utilize external knowledge, leading to repetitive or generic responses to unseen utterances. |
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| Challenge: | chit-chat models lack specificity, do not display a consistent personality and are often not very captivating. |
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| Challenge: | Existing methods suffer from incomprehensive persona tags that have unique and obscure meanings to describe human’s personality. |
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Getting To Know You: User Attribute Extraction from Dialogues (2020.lrec-1)
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| Challenge: | a new method to extract user attributes from dialogues is needed to improve user understanding. |
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| Challenge: | Existing models for personalized dialogue generation tend to be self-centered, with little care for the user in the dialogue. |
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Leveraging Implicit Feedback from Deployment Data in Dialogue (2024.eacl-short)
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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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Toward Stance-based Personas for Opinionated Dialogues (2020.findings-emnlp)
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| Challenge: | chit-chat neural models lacking specificity and coherence, argues a new study on stance-based personas . stancebased personal representations lack generalization capability, allowing agents to sustain personal points of view both within the same conversation and across different discussions. |
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You Truly Understand What I Need : Intellectual and Friendly Dialog Agents grounding Persona and Knowledge (2022.findings-emnlp)
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Jungwoo Lim, Myunghoon Kang, Yuna Hur, Seung Won Jeong, Jinsung Kim, Yoonna Jang, Dongyub Lee, Hyesung Ji, DongHoon Shin, Seungryong Kim, Heuiseok Lim
| Challenge: | Existing models that ground knowledge and persona at the same time are limited, leading to hallucination and a passive way of using personas. |
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Persona Expansion with Commonsense Knowledge for Diverse and Consistent Response Generation (2023.eacl-main)
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Donghyun Kim, Youbin Ahn, Wongyu Kim, Chanhee Lee, Kyungchan Lee, Kyong-Ho Lee, Jeonguk Kim, Donghoon Shin, Yeonsoo Lee
| Challenge: | Existing researches have focused on generating diverse and consistent responses based on personal traits. |
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