Proto-Gen: An end-to-end neural generator for persona and knowledge grounded response generation

Sougata Saha, Souvik Das, Rohini Srihari


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.
Anthology ID:
2022.ccgpk-1.2
Volume:
Proceedings of the 1st Workshop on Customized Chat Grounding Persona and Knowledge
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
CCGPK
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9–14
Language:
URL:
https://aclanthology.org/2022.ccgpk-1.2
DOI:
Bibkey:
Cite (ACL):
Sougata Saha, Souvik Das, and Rohini Srihari. 2022. Proto-Gen: An end-to-end neural generator for persona and knowledge grounded response generation. In Proceedings of the 1st Workshop on Customized Chat Grounding Persona and Knowledge, pages 9–14, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
Proto-Gen: An end-to-end neural generator for persona and knowledge grounded response generation (Saha et al., CCGPK 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.ccgpk-1.2.pdf
Data
FoCus