@inproceedings{lim-etal-2022-truly,
title = "You Truly Understand What {I} Need : Intellectual and Friendly Dialog Agents grounding Persona and Knowledge",
author = "Lim, Jungwoo and
Kang, Myunghoon and
Hur, Yuna and
Jeong, Seung Won and
Kim, Jinsung and
Jang, Yoonna and
Lee, Dongyub and
Ji, Hyesung and
Shin, DongHoon and
Kim, Seungryong and
Lim, Heuiseok",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.75",
doi = "10.18653/v1/2022.findings-emnlp.75",
pages = "1053--1066",
abstract = "To build a conversational agent that interacts fluently with humans, previous studies blend knowledge or personal profile into the pre-trained language model. However, the model that considers knowledge and persona at the same time is still limited, leading to hallucination and a passive way of using personas. We propose an effective dialogue agent that grounds external knowledge and persona simultaneously. The agent selects the proper knowledge and persona to use for generating the answers with our candidate scoring implemented with a poly-encoder. Then, our model generates the utterance with lesser hallucination and more engagingness utilizing retrieval augmented generation with knowledge-persona enhanced query. We conduct experiments on the persona-knowledge chat and achieve state-of-the-art performance in grounding and generation tasks on the automatic metrics. Moreover, we validate the answers from the models regarding hallucination and engagingness through human evaluation and qualitative results. We show our retriever{'}s effectiveness in extracting relevant documents compared to the other previous retrievers, along with the comparison of multiple candidate scoring methods. Code is available at \url{https://github.com/dlawjddn803/INFO}",
}
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<abstract>To build a conversational agent that interacts fluently with humans, previous studies blend knowledge or personal profile into the pre-trained language model. However, the model that considers knowledge and persona at the same time is still limited, leading to hallucination and a passive way of using personas. We propose an effective dialogue agent that grounds external knowledge and persona simultaneously. The agent selects the proper knowledge and persona to use for generating the answers with our candidate scoring implemented with a poly-encoder. Then, our model generates the utterance with lesser hallucination and more engagingness utilizing retrieval augmented generation with knowledge-persona enhanced query. We conduct experiments on the persona-knowledge chat and achieve state-of-the-art performance in grounding and generation tasks on the automatic metrics. Moreover, we validate the answers from the models regarding hallucination and engagingness through human evaluation and qualitative results. We show our retriever’s effectiveness in extracting relevant documents compared to the other previous retrievers, along with the comparison of multiple candidate scoring methods. Code is available at https://github.com/dlawjddn803/INFO</abstract>
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%0 Conference Proceedings
%T You Truly Understand What I Need : Intellectual and Friendly Dialog Agents grounding Persona and Knowledge
%A Lim, Jungwoo
%A Kang, Myunghoon
%A Hur, Yuna
%A Jeong, Seung Won
%A Kim, Jinsung
%A Jang, Yoonna
%A Lee, Dongyub
%A Ji, Hyesung
%A Shin, DongHoon
%A Kim, Seungryong
%A Lim, Heuiseok
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F lim-etal-2022-truly
%X To build a conversational agent that interacts fluently with humans, previous studies blend knowledge or personal profile into the pre-trained language model. However, the model that considers knowledge and persona at the same time is still limited, leading to hallucination and a passive way of using personas. We propose an effective dialogue agent that grounds external knowledge and persona simultaneously. The agent selects the proper knowledge and persona to use for generating the answers with our candidate scoring implemented with a poly-encoder. Then, our model generates the utterance with lesser hallucination and more engagingness utilizing retrieval augmented generation with knowledge-persona enhanced query. We conduct experiments on the persona-knowledge chat and achieve state-of-the-art performance in grounding and generation tasks on the automatic metrics. Moreover, we validate the answers from the models regarding hallucination and engagingness through human evaluation and qualitative results. We show our retriever’s effectiveness in extracting relevant documents compared to the other previous retrievers, along with the comparison of multiple candidate scoring methods. Code is available at https://github.com/dlawjddn803/INFO
%R 10.18653/v1/2022.findings-emnlp.75
%U https://aclanthology.org/2022.findings-emnlp.75
%U https://doi.org/10.18653/v1/2022.findings-emnlp.75
%P 1053-1066
Markdown (Informal)
[You Truly Understand What I Need : Intellectual and Friendly Dialog Agents grounding Persona and Knowledge](https://aclanthology.org/2022.findings-emnlp.75) (Lim et al., Findings 2022)
ACL
- Jungwoo Lim, Myunghoon Kang, Yuna Hur, Seung Won Jeong, Jinsung Kim, Yoonna Jang, Dongyub Lee, Hyesung Ji, DongHoon Shin, Seungryong Kim, and Heuiseok Lim. 2022. You Truly Understand What I Need : Intellectual and Friendly Dialog Agents grounding Persona and Knowledge. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1053–1066, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.