A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting
Submission Type: Regular Long Paper
Submission Track: Language Grounding to Vision, Robotics and Beyond
Submission Track 2: NLP Applications
Keywords: Text Classification, ChatGPT, Human-Evaluation, Low-Data
TL;DR: We define a novel task called Automated Strategy Translation, and build a langauge model which can outperform ChatGPT on this task.
Abstract: 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.
Submission Number: 2139
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