2021
EMNLP
EMNLP 2021
Analysis of Language Change in Collaborative Instruction Following
Abstract
AbstractWe 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.
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Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— convention formation
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Hot Topic Early Bird
— instruction following
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Agent Systems
Artificial Intelligence > Core AI > Human-AI Interaction
Artificial Intelligence > Core AI > Multi-Agent Systems
Machine Learning > Core Methods > Regression
Interdisciplinary > Linguistics > Computational Linguistics
Natural Language Processing > Applications > Dialogue Systems