@inproceedings{shichman-etal-2024-propbank,
title = "{P}rop{B}ank-Powered Data Creation: Utilizing Sense-Role Labelling to Generate Disaster Scenario Data",
author = "Shichman, Mollie Frances and
Bonial, Claire and
Hudson, Taylor A. and
Blodgett, Austin and
Ferraro, Francis and
Rudinger, Rachel",
editor = "Bonial, Claire and
Bonn, Julia and
Hwang, Jena D.",
booktitle = "Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.dmr-1.1",
pages = "1--10",
abstract = "For human-robot dialogue in a search-and-rescue scenario, a strong knowledge of the conditions and objects a robot will face is essential for effective interpretation of natural language instructions. In order to utilize the power of large language models without overwhelming the limited storage capacity of a robot, we propose PropBank-Powered Data Creation. PropBank-Powered Data Creation is an expert-in-the-loop data generation pipeline which creates training data for disaster-specific language models. We leverage semantic role labeling and Rich Event Ontology resources to efficiently develop seed sentences for fine-tuning a smaller, targeted model that could operate onboard a robot for disaster relief. We developed 32 sentence templates, which we used to make 2 seed datasets of 175 instructions for earthquake search and rescue and train derailment response. We further leverage our seed datasets as evaluation data to test our baseline fine-tuned models.",
}
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<abstract>For human-robot dialogue in a search-and-rescue scenario, a strong knowledge of the conditions and objects a robot will face is essential for effective interpretation of natural language instructions. In order to utilize the power of large language models without overwhelming the limited storage capacity of a robot, we propose PropBank-Powered Data Creation. PropBank-Powered Data Creation is an expert-in-the-loop data generation pipeline which creates training data for disaster-specific language models. We leverage semantic role labeling and Rich Event Ontology resources to efficiently develop seed sentences for fine-tuning a smaller, targeted model that could operate onboard a robot for disaster relief. We developed 32 sentence templates, which we used to make 2 seed datasets of 175 instructions for earthquake search and rescue and train derailment response. We further leverage our seed datasets as evaluation data to test our baseline fine-tuned models.</abstract>
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%0 Conference Proceedings
%T PropBank-Powered Data Creation: Utilizing Sense-Role Labelling to Generate Disaster Scenario Data
%A Shichman, Mollie Frances
%A Bonial, Claire
%A Hudson, Taylor A.
%A Blodgett, Austin
%A Ferraro, Francis
%A Rudinger, Rachel
%Y Bonial, Claire
%Y Bonn, Julia
%Y Hwang, Jena D.
%S Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F shichman-etal-2024-propbank
%X For human-robot dialogue in a search-and-rescue scenario, a strong knowledge of the conditions and objects a robot will face is essential for effective interpretation of natural language instructions. In order to utilize the power of large language models without overwhelming the limited storage capacity of a robot, we propose PropBank-Powered Data Creation. PropBank-Powered Data Creation is an expert-in-the-loop data generation pipeline which creates training data for disaster-specific language models. We leverage semantic role labeling and Rich Event Ontology resources to efficiently develop seed sentences for fine-tuning a smaller, targeted model that could operate onboard a robot for disaster relief. We developed 32 sentence templates, which we used to make 2 seed datasets of 175 instructions for earthquake search and rescue and train derailment response. We further leverage our seed datasets as evaluation data to test our baseline fine-tuned models.
%U https://aclanthology.org/2024.dmr-1.1
%P 1-10
Markdown (Informal)
[PropBank-Powered Data Creation: Utilizing Sense-Role Labelling to Generate Disaster Scenario Data](https://aclanthology.org/2024.dmr-1.1) (Shichman et al., DMR-WS 2024)
ACL