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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.239.pdf
Software:
 2021.findings-emnlp.239.Software.zip
Video:
 https://aclanthology.org/2021.findings-emnlp.239.mp4
Code
 lil-lab/cb-analysis