Papers by Helena Bonaldi
Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering (2022.emnlp-main)
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| Challenge: | a new approach to combat online hate speech is being proposed for NLG . existing methods to train NLG are limited to 2-turn interactions, while in real life, interactions can consist of multiple turns. |
| Approach: | They propose to combine human annotators with machine generated dialogues to create a dataset . DIALOCONAN is the first dataset comprising over 3000 fictitious multi-turn dialogues . |
| Outcome: | The proposed approach combines human experts over machine generated dialogues . it is the first dataset comprising over 3000 fictitious multi-turn dialogues between a hater and an NGO operator . |
NLP for Counterspeech against Hate and Misinformation (CSHAM) (2025.acl-tutorials)
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| Challenge: | tutorial aims to show how counterspeech is used to tackle abuse and misinformation by individuals, activists and organisations. |
| Approach: | tutorial aims to show how counterspeech is currently used to tackle abuse and misinformation . will also show how Natural Language Processing (NLP) and Generation (NLG) can be applied to automate its production. |
| Outcome: | The tutorial will bring diverse multidisciplinary perspectives to safety research . case studies from industry and public policy will be included . |
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. |
Is Safer Better? The Impact of Guardrails on the Argumentative Strength of LLMs in Hate Speech Countering (2024.emnlp-main)
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| Challenge: | Automated responses lack argumentative richness which characterises expert-produced counterspeech. |
| Approach: | They propose to automate counterspeech generation by investigating tension between helpfulness and harmlessness of LLMs and to assess whether presence of safety guardrails hinders quality of generations. |
| Outcome: | The proposed approach produces more cogent responses that lack argumentative richness which characterises expert-produced counterspeech. |
NLP for Counterspeech against Hate: A Survey and How-To Guide (2024.findings-naacl)
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| Challenge: | Recent studies have focused on the challenges of analysing, collecting, classifying, and automatically generating counterspeech, to reduce the huge burden of manually producing it. |
| Approach: | They propose a guide for doing research on counterspeech, with detailed examples and best practices that can be learnt from the NLP community. |
| Outcome: | The proposed strategies can reduce online and offline violence while preserving the freedom of speech of the users. |