A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting
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Abstract (arXiv)
Many real-world tasks involve a mixed-initiative setup, wherein humans and AI systems collaboratively perform a task. While significant work has been conducted towards enabling humans to specify, through language, exactly how an agent should complete a task (i.e., low-level specification), prior work lacks on interpreting the high-level strategic intent of the human commanders. Parsing strategic intent from language will allow autonomous systems to independently operate according to the user's plan without frequent guidance or instruction. In this paper, we build a computational interface capable of translating unstructured language strategies into actionable intent in the form of goals and constraints. Leveraging a game environment, we collect a dataset of over 1000 examples, mapping language strategies to the corresponding goals and constraints, and show that our model, trained on this dataset, significantly outperforms human interpreters in inferring strategic intent (i.e., goals and constraints) from language (p < 0.05). Furthermore, we show that our model (125M parameters) significantly outperforms ChatGPT for this task (p < 0.05) in a low-data setting.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 61 references that are in the map. First posted on arXiv 17 Aug 2022.
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References · 15
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2021CLIPort: What and Where Pathways for Robotic Manipulationcited by 622
- 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 377
- 2016Modular Multitask Reinforcement Learning with Policy Sketchescited by 203
- 2021TEACh: Task-driven Embodied Agents that Chatcited by 197
- 2019Using Natural Language for Reward Shaping in Reinforcement Learningcited by 114
- 2018Mapping Instructions to Actions in 3D Environments with Visual Goal Predictioncited by 101
- 2018Learning to Understand Goal Specifications by Modelling Rewardcited by 80
- 2019Executing Instructions in Situated Collaborative Interactionscited by 69
- 2019From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Followingcited by 67
- 2021Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translationcited by 57
- 2019Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flightcited by 43
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