Italian Counter Narrative Generation to Fight Online Hate Speech
Authors: Yi-Ling Chung, Serra Sinem Tekiro\u{g}lu, Marco Guerini (Università di Trento, Fondazione Bruno Kessler, 2020)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Counter Narratives are textual responses meant to withstand online hatred and prevent its spreading. The use of neural architectures for the generation of Counter Narratives (CNs) is beginning to be investigated by the NLP community. Still, the efforts were solely targeting English. In this paper, we try to fill the gap for Italian, studying how to implement CN generation approaches effectively. We experiment with an existing dataset of CNs and a novel language model, recently released for Italian, under several configurations, including zero and few shot learning. Results show that even for under-resourced languages, data augmentation strategies paired with large unsupervised LMs can held promising results.

Introduction
The rise of online Hate Speech (HS) brings along the need for combating strategies as it can trigger harmful psychological effects on the target groups and more crimes against them. While research studies have been widely focusing on hate speech detection methodologies for social media platforms AUTHOR, a recent line of research has taken the problem a step further by addressing the automatic generation of counter responses, aka counter narratives AUTHOR, in order to assist non-governmental organizations in their real-world online hatred combating efforts. An example of HS along with a possible CN are shown below: HS: Gli arabi sono tutti terroristi e vogliono conquistarci con la violenza e le bombe. Bisogna rispondere con il napalm. [Arabs are all terrorists and they want to conquer us with violence and bombs. We must respond with napalm.] CN: Essere di origine araba non significa essere terroristi, evitiamo generalizzazioni che portano solo ad altro odio. [Being of Arab descent does not mean being a terrorist, let's avoid generalizations that only lead to more hatred.] Despite the encouraging results of the counter narrative generation task, experiments have been limited to English due to the scarcity of hate speech / counter narrative data in other languages. In this paper, we investigate counter narrative generation for Italian as a case study where zero or only a small amount of task specific in-language data is available. We first explore the portability of generation across languages, considering that recent neural machine translation (NMT) systems have shown outstanding performances. We propose utilizing off-the-shelf NMT models to synthesize silver data from other languages, and fine-tuning AUTHOR, a recently developed GPT-2 based language model for Italian, on the silver data. We then examine the effect of combining silver with gold data on CN generation by experimenting with various gold data sizes. Our findings show that a proper combination of silver and gold data while fine-tuning LMs can drastically reduce the need for expert-annotator effort on target languages.

Conclusion
Counter narrative generation using neural architectures is beginning to be studied for hatred intervention. In this paper, we presented the first attempt of CN generation for Italian, investigating several variations of generation when gold data is limited or not available. Our experiments reveal that with simple data augmentation strategies paired with powerful LMs can bring promising results for under-resourced languages. As future work we plan to explore methods for improving specificity via the use of diverse types of knowledge (e.g., facts, events and named entities) from external resources. This would also mitigate the problem of the model possibly generating hallucinated content (i.e. non-existent or wrong content), see for example (Maynez et al., 2020; Nie et al., 2019). Finally, we plan to apply this approach to other hate phenomena such as antisemitism, homophobia, and misogyny.