Grounding NBA Matchup Summaries

Tadashi Nomoto


Abstract
The present paper summarizes an attempt we made to meet a shared task challenge on grounding machine-generated summaries of NBA matchups (https://github.com/ehudreiter/accuracySharedTask.git). In the first half, we discuss methods and in the second, we report results, together with a discussion on what feature may have had an effect on the performance.
Anthology ID:
2021.inlg-1.28
Volume:
Proceedings of the 14th International Conference on Natural Language Generation
Month:
August
Year:
2021
Address:
Aberdeen, Scotland, UK
Editors:
Anya Belz, Angela Fan, Ehud Reiter, Yaji Sripada
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
276–281
Language:
URL:
https://aclanthology.org/2021.inlg-1.28
DOI:
10.18653/v1/2021.inlg-1.28
Bibkey:
Cite (ACL):
Tadashi Nomoto. 2021. Grounding NBA Matchup Summaries. In Proceedings of the 14th International Conference on Natural Language Generation, pages 276–281, Aberdeen, Scotland, UK. Association for Computational Linguistics.
Cite (Informal):
Grounding NBA Matchup Summaries (Nomoto, INLG 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.inlg-1.28.pdf
Code
 ehudreiter/accuracysharedtask
Data
RotoWire