Learning Cause Identifiers from Annotator Rationales
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1758-1762.
Abstract
In the aviation safety research domain, cause identification refers to the task of identifying the possible causes responsible for the incident describedin an aviation safety incident report. This task presents a number of challenges, including the scarcity of labeled data and the difficulties in finding the relevant portions of the text. We investigate the use of annotator rationales to overcome these challenges, proposing several new ways of utilizing rationales and showing that through judicious use of the rationales, it is possible to achieve significant improvement over a unigram SVM baseline.
@inproceedings{ijcai2011p295,
title = {Learning Cause Identifiers from Annotator Rationales},
author = {Abedin, Muhammad Arshad Ul and Ng, Vincent and Khan, Latifur},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1758--1762},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-295},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-295},
}
TY - CPAPER TI - Learning Cause Identifiers from Annotator Rationales AU - Abedin, Muhammad Arshad Ul AU - Ng, Vincent AU - Khan, Latifur T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1758 EP - 1762 DO - 10.5591/978-1-57735-516-8/IJCAI11-295 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-295 ER -Copied
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