Summary Grounded Conversation Generation

Chulaka Gunasekara, Guy Feigenblat, Benjamin Sznajder, Sachindra Joshi, David Konopnicki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Many conversation datasets have been constructed in the recent years using crowd-sourcing. However, the data collection process can be time consuming and presents many challenges to ensure data quality. Since language generation has improved immensely in recent years with the advancement of pretrained language models, we investigate how such models can be utilized to generate entire conversations, given only a summary of a conversation as the input. We explore three approaches to generate summary grounded conversations, and evaluate the generated conversations using automatic measures and human judgements. We also show that the accuracy of conversation summarization can be improved by augmenting a conversation summarization dataset with generated conversations.

Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationACL-IJCNLP 2021
EditorsChengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
PublisherAssociation for Computational Linguistics (ACL)
Pages3748-3756
Number of pages9
ISBN (Electronic)9781954085541
StatePublished - 2021
Externally publishedYes
EventFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021 - Virtual, Online
Duration: 1 Aug 20216 Aug 2021

Publication series

NameFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Conference

ConferenceFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021
CityVirtual, Online
Period1/08/216/08/21

Bibliographical note

Publisher Copyright:
© 2021 Association for Computational Linguistics

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