Analysis of Language Change in Collaborative Instruction Following
Anna Effenberger, Rhia Singh, Eva Yan, Alane Suhr, Yoav Artzi
Abstract
We analyze language change over time in a collaborative, goal-oriented instructional task, where utility-maximizing participants form conventions and increase their expertise. Prior work studied such scenarios mostly in the context of reference games, and consistently found that language complexity is reduced along multiple dimensions, such as utterance length, as conventions are formed. In contrast, we find that, given the ability to increase instruction utility, instructors increase language complexity along these previously studied dimensions to better collaborate with increasingly skilled instruction followers.- Anthology ID:
- 2021.findings-emnlp.239
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2803–2811
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.239
- DOI:
- 10.18653/v1/2021.findings-emnlp.239
- Cite (ACL):
- Anna Effenberger, Rhia Singh, Eva Yan, Alane Suhr, and Yoav Artzi. 2021. Analysis of Language Change in Collaborative Instruction Following. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2803–2811, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Analysis of Language Change in Collaborative Instruction Following (Effenberger et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2021.findings-emnlp.239.pdf
- Code
- lil-lab/cb-analysis