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)


Related Open Educational Resources

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.
Lecture popularity: You need to login to cast your vote.


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.

See Also:

Download slides icon Download slides: parlaCLARIN2018_naderi_defence_strategies_01.pdf (711.1┬áKB)

Help icon Streaming Video Help

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:

make sure you have javascript enabled or clear this field: