Counterspeech the ultimate shield! Multi-Conditioned Counterspeech Generation through Attributed Prefix Learning
Aswini Kumar, Anil Bandhakavi, Tanmoy Chakraborty

TL;DR
This paper introduces HiPPrO, a hierarchical prefix learning framework that enhances counterspeech generation by considering multiple attributes simultaneously, leading to more relevant and effective responses against hate speech.
Contribution
The paper proposes a novel two-stage hierarchical prefix learning framework, HiPPrO, that incorporates multiple attributes for improved counterspeech generation, extending previous single-attribute models.
Findings
38% improvement in intent conformity
Enhanced relevance and appropriateness of counterspeech
Open-sourced code and dataset for reproducibility
Abstract
Counterspeech has proven to be a powerful tool to combat hate speech online. Previous studies have focused on generating counterspeech conditioned only on specific intents (single attributed). However, a holistic approach considering multiple attributes simultaneously can yield more nuanced and effective responses. Here, we introduce HiPPrO, Hierarchical Prefix learning with Preference Optimization, a novel two-stage framework that utilizes the effectiveness of attribute-specific prefix embedding spaces hierarchically optimized during the counterspeech generation process in the first phase. Thereafter, we incorporate both reference and reward-free preference optimization to generate more constructive counterspeech. Furthermore, we extend IntentCONANv2 by annotating all 13,973 counterspeech instances with emotion labels by five annotators. HiPPrO leverages hierarchical prefix…
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Taxonomy
TopicsSpeech Recognition and Synthesis · Natural Language Processing Techniques · Topic Modeling
