Basque and Spanish Counter Narrative Generation: Data Creation and Evaluation (2024.lrec-main)
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| Challenge: | Davidson et al.: hate speech is a growing media presence, but research on generating CNs has been limited . he says a new dataset for CN generation is available for basque and spanish . this dataset is based on a multilingual encoder-decoder model . |
| Approach: | They propose a new Basque and Spanish dataset for automatic CN generation . they use machine translation and professional post-edition to generate CNs in both languages . |
| Outcome: | The proposed datasets show that training on post-edited data improves generation over monolingual settings . similar results in zero-shot crosslingual evaluations show multilingual data augmentation outperforms training in English and Spanish . |
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María Estrella Vallecillo Rodríguez, Maria Victoria Cantero Romero, Isabel Cabrera De Castro, Arturo Montejo Ráez, María Teresa Martín Valdivia
| Challenge: | a new study evaluates the performance of GPT-based models to generate CNs for hate speech in Spanish . a growing number of social interactions through digital platforms have led to inappropriate behavior . |
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| Outcome: | The proposed models outperform human models in most instances, the authors say . the results will be made available to the research 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. |
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CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (P19-1)
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| Challenge: | Davidson et al., 2017): social media platforms and governmental organizations have taken steps to tackle hate speech . Davidson and Norton, 2017: a dataset of hate-speech/counter-narrative pairs is created . authors: identifying hate speech is challenging for the broadness and nuances in cultures and languages . |
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BIASEDTALES-ML: A Multilingual Dataset for Analyzing Narrative Attribute Distributions in LLM-Generated Stories (2026.findings-acl)
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| Challenge: | Existing studies on the use of Large Language Models (LLMs) focus primarily on English, leaving the cross-lingual generalization of aligned behavior underexplored. |
| Approach: | They propose a structured generator-extractor pipeline and a multi-dimensional distributional analysis framework to examine how narrative attributes vary across languages, models, and social conditions. |
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A LLM-based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation (2024.findings-emnlp)
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| Challenge: | Existing methods for evaluating CNs are expensive, time-consuming, and subjective, but lack a universal truth and the lack of a 'universal truth' . |
| Approach: | They propose a model ranking pipeline based on pairwise comparisons of generated CNs from different models organized in a tournament-style format to improve the evaluation process. |
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Generating Counter Narratives against Online Hate Speech: Data and Strategies (2020.acl-main)
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| Challenge: | Hate Speech (HS) is a pervasive issue that spreads quickly and widely . research has focused on avoiding undesired effects that come with content moderation . |
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Towards Knowledge-Grounded Counter Narrative Generation for Hate Speech (2021.findings-acl)
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| Challenge: | Existing approaches to combat online hatred using informed textual responses - called counter narratives - produce generic/repetitive responses and lack grounded and up-to-date evidence such as facts, statistics, or examples. |
| Approach: | They propose to automatically generate counter narratives using an external knowledge repository to provide more informative content to fight online hatred. |
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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. |
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Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation (2026.acl-long)
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Qianli Wang, Van Bach Nguyen, Yihong Liu, Fedor Splitt, Nils Feldhus, Christin Seifert, Hinrich Schuetze, Sebastian Möller, Vera Schmitt
| Challenge: | Large language models excel at generating English counterfactuals but their effectiveness in generating multilingual counterfacts remains unclear. |
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Contextualized Graph Representations for Generating Counter-Narratives against Hate Speech (2024.findings-emnlp)
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| Challenge: | Hate speech (HS) is a widespread problem in society with severe repercussions at both personal and societal levels. |
| Approach: | They propose to incorporate conversational history into CNs to confront biases and stereotypes driving hateful narratives. |
| Outcome: | The proposed strategies outperform existing methods on comparing graphical and text representations with varying degrees of context. |