CounterGeDi: A controllable approach to generate polite, detoxified and
emotional counterspeech
- URL: http://arxiv.org/abs/2205.04304v1
- Date: Mon, 9 May 2022 14:10:57 GMT
- Title: CounterGeDi: A controllable approach to generate polite, detoxified and
emotional counterspeech
- Authors: Punyajoy Saha, Kanishk Singh, Adarsh Kumar, Binny Mathew and Animesh
Mukherjee
- Abstract summary: We propose CounterGeDi to guide the generation of a DialoGPT model toward more polite, detoxified, and emotionally laden counterspeech.
We generate counterspeech using three datasets and observe significant improvement across different attribute scores.
- Score: 7.300229659237878
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, many studies have tried to create generation models to assist
counter speakers by providing counterspeech suggestions for combating the
explosive proliferation of online hate. However, since these suggestions are
from a vanilla generation model, they might not include the appropriate
properties required to counter a particular hate speech instance. In this
paper, we propose CounterGeDi - an ensemble of generative discriminators (GeDi)
to guide the generation of a DialoGPT model toward more polite, detoxified, and
emotionally laden counterspeech. We generate counterspeech using three datasets
and observe significant improvement across different attribute scores. The
politeness and detoxification scores increased by around 15% and 6%
respectively, while the emotion in the counterspeech increased by at least 10%
across all the datasets. We also experiment with triple-attribute control and
observe significant improvement over single attribute results when combining
complementing attributes, e.g., politeness, joyfulness and detoxification. In
all these experiments, the relevancy of the generated text does not deteriorate
due to the application of these controls
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