In contrast to classical cognitive science which studied brains in isolation,
ecological approaches focused on the role of the body and environment in
shaping cognition. Similarly, in this thesis we adopt an ecological approach to
grounded natural language understanding (NLU) research. Grounded language
understanding studies language understanding systems situated in the context of
events, actions and precepts in naturalistic/simulated virtual environments.
Where classic research tends to focus on designing new models and optimization
methods while treating environments as given, we explore the potential of
environment design for improving data collection and model development. We
developed novel training and annotation approaches for procedural text
understanding based on text-based game environments. We also drew upon embodied
cognitive linguistics literature to propose a roadmap for grounded NLP
research, and to inform the development of a new benchmark for measuring the
progress of large language models on challenging commonsense reasoning tasks.
We leveraged the richer supervision provided by text-based game environments to
develop Breakpoint Transformers, a novel approach to modeling intermediate
semantic information in long narrative or procedural texts. Finally, we
integrated theories on the role of environments in collective human
intelligence to propose a design for AI-augmented "social thinking
environments" for knowledge workers like scientists.

通过生态学方法研究自然语言理解，利用基于基于文本游戏环境的训练和注释方法来改进数据收集和模型开发，提出了一种新的测试基准来衡量大型语言模型在常识推理任务中的进展，并提出了 AI 辅助的 “社交思维环境” 设计用于科学家等知识工作者。