@inproceedings{tambwekar-etal-2023-computational,
title = "A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting",
author = "Tambwekar, Pradyumna and
Dodeja, Lakshita and
Vaska, Nathan and
Xu, Wei and
Gombolay, Matthew",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.853",
doi = "10.18653/v1/2023.findings-emnlp.853",
pages = "12801--12819",
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 {\textless} 0.05). Furthermore, we show that our model (125M parameters) significantly outperforms ChatGPT for this task (p {\textless} 0.05) in a low-data setting.",
}
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<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 \textless 0.05). Furthermore, we show that our model (125M parameters) significantly outperforms ChatGPT for this task (p \textless 0.05) in a low-data setting.</abstract>
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%0 Conference Proceedings
%T A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting
%A Tambwekar, Pradyumna
%A Dodeja, Lakshita
%A Vaska, Nathan
%A Xu, Wei
%A Gombolay, Matthew
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F tambwekar-etal-2023-computational
%X 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 \textless 0.05). Furthermore, we show that our model (125M parameters) significantly outperforms ChatGPT for this task (p \textless 0.05) in a low-data setting.
%R 10.18653/v1/2023.findings-emnlp.853
%U https://aclanthology.org/2023.findings-emnlp.853
%U https://doi.org/10.18653/v1/2023.findings-emnlp.853
%P 12801-12819
Markdown (Informal)
[A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting](https://aclanthology.org/2023.findings-emnlp.853) (Tambwekar et al., Findings 2023)
ACL