@inproceedings{saha-etal-2022-proto,
title = "Proto-Gen: An end-to-end neural generator for persona and knowledge grounded response generation",
author = "Saha, Sougata and
Das, Souvik and
Srihari, Rohini",
editor = "Lim, Heuiseok and
Kim, Seungryong and
Lee, Yeonsoo and
Lin, Steve and
Seo, Paul Hongsuck and
Suh, Yumin and
Jang, Yoonna and
Lim, Jungwoo and
Hur, Yuna and
Son, Suhyune",
booktitle = "Proceedings of the 1st Workshop on Customized Chat Grounding Persona and Knowledge",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ccgpk-1.2",
pages = "9--14",
abstract = "In this paper we detail the implementation of Proto-Gen, an end-to-end neural response generator capable of selecting appropriate persona and fact sentences from available options, and generating persona and fact grounded responses. Incorporating a novel interaction layer in an encoder-decoder architecture, Proto-Gen facilitates learning dependencies between facts, persona and the context, and outperforms existing baselines on the FoCus dataset for both the sub-tasks of persona and fact selection, and response generation. We further fine tune Proto-Gen{'}s hyperparameters, and share our results and findings.",
}
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<abstract>In this paper we detail the implementation of Proto-Gen, an end-to-end neural response generator capable of selecting appropriate persona and fact sentences from available options, and generating persona and fact grounded responses. Incorporating a novel interaction layer in an encoder-decoder architecture, Proto-Gen facilitates learning dependencies between facts, persona and the context, and outperforms existing baselines on the FoCus dataset for both the sub-tasks of persona and fact selection, and response generation. We further fine tune Proto-Gen’s hyperparameters, and share our results and findings.</abstract>
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%0 Conference Proceedings
%T Proto-Gen: An end-to-end neural generator for persona and knowledge grounded response generation
%A Saha, Sougata
%A Das, Souvik
%A Srihari, Rohini
%Y Lim, Heuiseok
%Y Kim, Seungryong
%Y Lee, Yeonsoo
%Y Lin, Steve
%Y Seo, Paul Hongsuck
%Y Suh, Yumin
%Y Jang, Yoonna
%Y Lim, Jungwoo
%Y Hur, Yuna
%Y Son, Suhyune
%S Proceedings of the 1st Workshop on Customized Chat Grounding Persona and Knowledge
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F saha-etal-2022-proto
%X In this paper we detail the implementation of Proto-Gen, an end-to-end neural response generator capable of selecting appropriate persona and fact sentences from available options, and generating persona and fact grounded responses. Incorporating a novel interaction layer in an encoder-decoder architecture, Proto-Gen facilitates learning dependencies between facts, persona and the context, and outperforms existing baselines on the FoCus dataset for both the sub-tasks of persona and fact selection, and response generation. We further fine tune Proto-Gen’s hyperparameters, and share our results and findings.
%U https://aclanthology.org/2022.ccgpk-1.2
%P 9-14
Markdown (Informal)
[Proto-Gen: An end-to-end neural generator for persona and knowledge grounded response generation](https://aclanthology.org/2022.ccgpk-1.2) (Saha et al., CCGPK 2022)
ACL