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
- 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)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2021.inlg-1.28.pdf
- Code
- ehudreiter/accuracysharedtask
- Data
- RotoWire