TY - GEN
T1 - DimonGen
T2 - 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
AU - Liu, Chenzhengyi
AU - Huang, Jie
AU - Zhu, Kerui
AU - Chang, Kevin Chen Chuan
N1 - This material is based upon work supported by the National Science Foundation IIS 16-19302 and IIS 16-33755, Zhejiang University ZJU Research 083650, IBM-Illinois Center for Cognitive Computing SystemsResearch (C3SR) and IBM-Illinois Discovery Accelerator Institute (IIDAI), gift grants from eBay and Microsoft Azure, UIUC OVCR CCIL Planning Grant 434S34, UIUC CSBS Small Grant 434C8U, and UIUC New Frontiers Initiative. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the funding agencies.
This material is based upon work supported by the National Science Foundation IIS 16-19302 and IIS 16-33755, Zhejiang University ZJU Research 083650, IBM-Illinois Center for Cognitive Computing Systems Research (C3SR) and IBM-Illinois Discovery Accelerator Institute (IIDAI), gift grants from eBay and Microsoft Azure, UIUC OVCR CCIL Planning Grant 434S34, UIUC CSBS Small Grant 434C8U, and UIUC New Frontiers Initiative. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the funding agencies.
PY - 2023
Y1 - 2023
N2 - In this paper, we propose DimonGen, which aims to generate diverse sentences describing concept relationships in various everyday scenarios. To support this, we first create a benchmark dataset for this task by adapting the existing CommonGen dataset. We then propose a two-stage model called MoREE to generate the target sentences. MoREE consists of a mixture of retrievers model that retrieves diverse context sentences related to the given concepts, and a mixture of generators model that generates diverse sentences based on the retrieved contexts. We conduct experiments on the DimonGen task and show that MoREE outperforms strong baselines in terms of both the quality and diversity of the generated sentences. Our results demonstrate that MoREE is able to generate diverse sentences that reflect different relationships between concepts, leading to a comprehensive understanding of concept relationships.
AB - In this paper, we propose DimonGen, which aims to generate diverse sentences describing concept relationships in various everyday scenarios. To support this, we first create a benchmark dataset for this task by adapting the existing CommonGen dataset. We then propose a two-stage model called MoREE to generate the target sentences. MoREE consists of a mixture of retrievers model that retrieves diverse context sentences related to the given concepts, and a mixture of generators model that generates diverse sentences based on the retrieved contexts. We conduct experiments on the DimonGen task and show that MoREE outperforms strong baselines in terms of both the quality and diversity of the generated sentences. Our results demonstrate that MoREE is able to generate diverse sentences that reflect different relationships between concepts, leading to a comprehensive understanding of concept relationships.
UR - http://www.scopus.com/inward/record.url?scp=85162816628&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85162816628&partnerID=8YFLogxK
U2 - 10.18653/v1/2023.acl-long.260
DO - 10.18653/v1/2023.acl-long.260
M3 - Conference contribution
AN - SCOPUS:85162816628
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 4719
EP - 4731
BT - Long Papers
PB - Association for Computational Linguistics (ACL)
Y2 - 9 July 2023 through 14 July 2023
ER -