Learning Cause Identifiers from Annotator Rationales

Muhammad Arshad Ul Abedin, Vincent Ng, and Latifur Rahman Khan.
Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pp. 1758-1763, 2011.

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In the aviation safety research domain, cause identification refers to the task of identifying the possible causes responsible for the incident described in 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.


The human annotation used in this paper is available from this page.

BibTeX entry

  author = {Muhammad Arshad Ul Abedin and Vincent Ng and Latifur Rahman Khan},
  title = {Learning Cause Identifiers from Annotator Rationales},
  booktitle = {Proceedings of the 22nd International Joint Conference on Artificial Intelligence},
  pages = {1758--1763},
  year = 2011