HiPPrO generates counterspeech conditioned on both a strategy and an emotion via hierarchical prefix learning plus preference optimization, and reports gains on a new emotion-labeled corpus.
Overview of the HASOC Subtrack at FIRE 2023: Identification of Tokens Contributing to Explicit Hate in English by Span Detection
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
As hate speech continues to proliferate on the web, it is becoming increasingly important to develop computational methods to mitigate it. Reactively, using black-box models to identify hateful content can perplex users as to why their posts were automatically flagged as hateful. On the other hand, proactive mitigation can be achieved by suggesting rephrasing before a post is made public. However, both mitigation techniques require information about which part of a post contains the hateful aspect, i.e., what spans within a text are responsible for conveying hate. Better detection of such spans can significantly reduce explicitly hateful content on the web. To further contribute to this research area, we organized HateNorm at HASOC-FIRE 2023, focusing on explicit span detection in English Tweets. A total of 12 teams participated in the competition, with the highest macro-F1 observed at 0.58.
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Counterspeech the ultimate shield! Multi-Conditioned Counterspeech Generation through Attributed Prefix Learning
HiPPrO generates counterspeech conditioned on both a strategy and an emotion via hierarchical prefix learning plus preference optimization, and reports gains on a new emotion-labeled corpus.