Leveraging Pre-existing Resources for Data-Efficient Counter-Narrative Generation in Korean (2024.lrec-main)
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| Challenge: | Existing datasets and methods for detecting hate speech are limited by resource-intensive nature and only focus on the primary language. |
| Approach: | They propose a Korean Hate Speech Counter Punch (KHSCP) method that generates fact-based responses to hate speech in the Korean language and propose to use existing resources to overcome data scarcity. |
| Outcome: | The proposed method can overcome data scarcity in low-resource environments by leveraging existing resources. |
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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. |
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| Challenge: | Existing studies on generating hate speech/counter narratives have failed to reach high-quality datasets. |
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| Challenge: | generative data augmentation has been shown to be effective in offensive language detection but the potential for bias injection has not been investigated. |
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Jaehyung Seo, Seounghoon Lee, Chanjun Park, Yoonna Jang, Hyeonseok Moon, Sugyeong Eo, Seonmin Koo, Heuiseok Lim
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High-quality argumentative information in low resources approaches improve counter-narrative generation (2023.findings-emnlp)
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| Challenge: | a recent study shows that fine-tuning improves the performance of language models . large language models generate acceptable texts in a number of scenarios, a study shows . |
| Approach: | They show that fine-tuning improves the task of hate speech counter-narrative generation . they provide a subset of arguments and a good base model is required for the fine-uning to have a positive impact. |
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| Challenge: | flaming or trolling in online communities is considered hostile behavior . a dataset of hate speech examples can be useful for detecting toxic or pejorative expressions . annotating on existing web text has several limitations that deter the dataset's reliability . |
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Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech (2021.findings-acl)
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| Challenge: | Off-the-shelf methods to generate hate speech are limited in that they generate repetitive and safe responses regardless of the hate speech. |
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