RSSN at Multilingual Counterspeech Generation: Leveraging Lightweight Transformers for Efficient and Context-Aware Counter-Narrative Generation (2025)

Abstract: This paper presents a system for counter-speech generation, developed for the COLING 2025 shared task. By leveraging lightweight transformer models, DistilBART and T5-small, we optimize computational efficiency while maintaining strong performance. The work includes an in-depth analysis of a multilingual dataset, addressing hate speech instances across diverse languages and target groups. Through systematic error analysis, we identify challenges such as lack of specificity and context misinterpretation in generated counter-narratives. Evaluation metrics like BLEU, ROUGE, and BERTScore demonstrate the effectiveness of our approaches, while comparative insights highlight complementary strengths in fluency, contextual integration, and creativity. Future directions focus on enhancing preprocessing, integrating external knowledge sources, and improving scalability.

## introduction the pervasive spread of hate speech (hs) across online platforms poses a significant challenge to fostering respectful and inclusive digital discourse. counter-speech (cn) has emerged as a proactive and constructive strategy to address hate speech, offering alternative narratives that challenge biases while promoting empathy and inclusivity. however, the generation of effective counter-speech demands solutions that balance contextual relevance, linguistic coherence, and adaptability to diverse scenarios, making it a complex yet critical task. this paper presents our system developed for the coling 2025 shared task, designed to generate contextually appropriate counter-narratives efficiently. our approach leverages lightweight transformer-based architectures, specifically distilbart and t5-small, to achieve a balance between computational efficiency and performance. by utilizing these compact models, we aim to demonstrate that high-quality counter-narrative generation can be achieved without the computational overhead typically associated with larger architectures. to inform and optimize the generation process, we conducted a thorough analysis of the multilingual dataset provided for the shared task, with a specific focus on the english subset. this choice was motivated by the need to maintain consistency across training, validation, and evaluation phases while ensuring robust and interpretable results. our methodology incorporates structured preprocessing, integrating key components such as the target group (target), hate speech instance (hs), and background knowledge (kn) to create inputs that preserve contextual richness. this study underscores the viability of lightweight transformer models in generating high-quality counter-narratives, offering a scalable solution for addressing hate speech in resourceconstrained scenarios. through detailed evaluation and contextualized comparisons, we demonstrate the potential of these models for impactful and efficient counter-speech generation ## related work the development of counter-narratives (cns) as a strategy to combat hate speech has been the focus of several research efforts. early work highlights the challenges in evaluating cns, as traditional metrics like bleu and rouge often fail to align with human judgment. to address this, frameworks leveraging large language models @xcite (llms) for multi-aspect evaluation have emerged, providing interpretable and human-aligned assessments based on guidelines from counter-narrative specialized organizations. knowledge-grounded cn generation @xcite has also gained attention, with approaches integrating external repositories to produce contextually rich and factually accurate cns, addressing issues of generic or repetitive outputs. additionally, comparative studies of pre-trained language models (tekiroglu et al., 2022) have identified autoregressive models with stochastic decoding as particularly effective for cn generation. these studies emphasize the importance of task-specific training data and the use of post-editing pipelines to enhance quality and adaptability, particularly in addressing unseen hate targets. together, these advancements contribute significantly to the development of robust and context-aware cn generation systems. ## dataset analysis the dataset, land-fbk ml_mtconan_kn, comprises training (1,584 samples), validation (400 samples), and test (400 samples) splits across four languages: english (en), spanish (es), italian (it), and basque (eu). each entry includes a hate speech instance (hs), corresponding counter-narrative (cn), background knowledge sentences (kn), target group (target), and language (lang). the dataset covers multiple targets of hate: jews, lgbt+, migrants, muslims, people of color (poc), and women. ## conclusion and future work this paper demonstrates the effectiveness of lightweight transformer models, such as t5-small and distilbart, for counter-speech generation. while our approach achieved competitive performance across multiple metrics, limitations in contextual specificity and diversity were identified. future work will focus on: our findings emphasize the importance of balancing computational efficiency with output quality, paving the way for further advancements in counter-speech generation systems.