Papers by Margherita Fanton

2 papers
Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech (2021.acl-long)

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Challenge: Existing studies on generating hate speech/counter narratives have failed to reach high-quality datasets.
Approach: They propose a human-in-the-loop data collection methodology that refines a generative language model iteratively by using its own data from previous loops to generate new training samples.
Outcome: The proposed method is the only expert-based multi-target HS/CN dataset available to the community.
Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study (2022.findings-acl)

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Challenge: Autoregressive models combined with stochastic decodings are the most promising for generating CNs with regard to an unseen target of hate.
Approach: They propose to use pre-trained language models to generate counter-narratives in English by adding an automatic post-editing step to refine generated CNs.
Outcome: The proposed pipeline could be used to generate counter-narratives in English using pre-trained language models and stochastic decoding mechanisms.

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