High-quality argumentative information in low resources approaches improve counter-narrative generation
Submission Type: Regular Long Paper
Submission Track: Sentiment Analysis, Stylistic Analysis, and Argument Mining
Submission Track 2: Theme Track: Large Language Models and the Future of NLP
Keywords: counter-narratives, argument mining, finetuning
TL;DR: Counter-narrative generation can be significantly improved with very few, high-quality examples and argumentative information
Abstract: It has been shown that high quality fine-tuning boosts the performance of language models, even if the size of the fine-tuning is small.
In this work we show how highly targeted fine-tuning improves the task of hate speech counter-narrative generation in user-generated text, even for very small sizes of training (1722 counter-narratives for English and 355 for Spanish). Providing a small subset of examples focusing on single argumentative strategies, together with the argumentative analysis relevant to that strategy, yields counter-narratives that are as satisfactory as providing the whole set of counter-narratives.
We also show that a good base model is required for the fine-tuning to have a positive impact. Indeed, for Spanish, the counter-narratives obtained without fine-tuning are mostly unacceptable, and, while fine-tuning improves their overall quality, the performance still remains quite unsatisfactory.
Submission Number: 5806
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