# A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting

arXiv: https://arxiv.org/abs/2208.08374 · HTML: https://mlsanity.com/paper/2208.08374/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 15 of 15)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [CLIPort: What and Where Pathways for Robotic Manipulation](https://mlsanity.com/paper/2109.12098/) (2021)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Modular Multitask Reinforcement Learning with Policy Sketches](https://mlsanity.com/paper/1611.01796/) (2016)
- [TEACh: Task-driven Embodied Agents that Chat](https://mlsanity.com/paper/2110.00534/) (2021)
- [Using Natural Language for Reward Shaping in Reinforcement Learning](https://mlsanity.com/paper/1903.02020/) (2019)
- [Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction](https://mlsanity.com/paper/1809.00786/) (2018)
- [Learning to Understand Goal Specifications by Modelling Reward](https://mlsanity.com/paper/1806.01946/) (2018)
- [Executing Instructions in Situated Collaborative Interactions](https://mlsanity.com/paper/1910.03655/) (2019)
- [From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following](https://mlsanity.com/paper/1902.07742/) (2019)
- [Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation](https://mlsanity.com/paper/2106.05093/) (2021)
- [Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight](https://mlsanity.com/paper/1910.09664/) (2019)
- [Improving Grounded Natural Language Understanding through Human-Robot Dialog](https://mlsanity.com/paper/1903.00122/) (2019)
- [Safe Reinforcement Learning with Natural Language Constraints](https://mlsanity.com/paper/2010.05150/) (2020)
- [On Ensuring that Intelligent Machines Are Well-Behaved](https://mlsanity.com/paper/1708.05448/) (2017)

## Built on by (most cited first, 1 of 1)

- [Constraints-of-Thought: A Framework for Constrained Reasoning in Language-Model-Guided Search](https://mlsanity.com/paper/2510.08992/) (2025)

## 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.