Automatically labeled data generation for classification of reputation defence strategies
author: Nona Naderi,
Department of Computer Science, University of Toronto
published: May 30, 2018, recorded: May 2018, views: 561
released under terms of: Creative Commons Attribution (CC-BY)
published: May 30, 2018, recorded: May 2018, views: 561
released under terms of: Creative Commons Attribution (CC-BY)
Slides
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
Reputation defence is a form of persuasive tactic that is used in various social settings especially in political situations. Detection of reputation defence strategy is a novel task that could help in argument reasoning. Here, we propose an approach to automatically label training data for reputation defence strategies. We experimented with about 14,000 pairs of questions and answers from the Canadian Parliament, and automatically created a corpus of questions and answers annotated with reputation defence strategies. We further assess the quality of the automatically labeled data.
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: