Papers by Yi-Ling Chung
On the Effectiveness of Adversarial Robustness for Abuse Mitigation with Counterspeech (2024.naacl-long)
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| Challenge: | Recent work on automated counterspeech systems focused on synthetic data but rarely looked into how the public deals with abuse. |
| Approach: | They propose to curate a new dataset of abuse and replies from footballers for study of public figure abuse and use it to examine how models can handle adversarial attacks. |
| Outcome: | The proposed model is robust against adversarial attacks across domains and can handle abuse in the real world. |
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. |
| Outcome: | The proposed pipeline can generate suitable and informative counter narratives in in-domain and cross-domain settings. |
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 . |
| Approach: | They propose to use large scale unsupervised language models to generate responses to hate effectively using large scale models. |
| Outcome: | The proposed methods lack quality data and produce generic/repetitive responses. |
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 . |
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 . |
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 . |
| Approach: | They propose to build a large-scale, multilingual, expert-based dataset of hate-speech/counter-narrative pairs . they provide additional annotations about expert demographics, hate and response type . |
| Outcome: | The proposed dataset provides an analysis of hate-speech/counter-narrative pairs in three languages. |
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. |