意大利反叙事一代打击网络仇恨言论

Yi-Ling Chung, Serra Sinem Tekiroğlu, Marco Guerini
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引用次数: 11

摘要

英语。反叙事是一种文字回应,旨在抵御网络仇恨并防止其蔓延。NLP社区开始研究使用神经结构生成反叙事(CNs)。不过,这些努力只针对英语。在本文中,我们试图填补意大利语的空白,研究如何有效地实现CN生成方法。我们用一个现有的神经网络数据集和一个新的语言模型(最近发布的意大利语语言模型)在几种配置下进行了实验,包括零和少镜头学习。结果表明,即使对于资源不足的语言,数据增强策略与大型无监督LMs配对也可以获得有希望的结果。意大利语。“控制叙事”是一种对抗“测试电压”的方法,与“在线音频”形成对比,防止“音频”扩散。“NLP社区”是一个由“建筑神经网络”和“建筑神经网络”组成的工作室。Tuttavia, gli sforzi是一个独立的国家,它是一个独立的国家。在有关味觉、味觉、味觉和味觉、味觉和味觉的问题上,最奇怪的是,执行效率的方法是联合国的通用方法。在不同的配置下,通过对数据集的分析,建立了基于意大利语的语言学习模型,实现了零次学习。通过对不同语言之间的语言关系进行分析,数据增强策略为语言关系提供了一种潜在的语言关系模型,并提供了一种新的语言关系模型。本文版权所有©2020。在知识共享许可国际署名4.0 (CC BY 4.0)下允许使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Italian Counter Narrative Generation to Fight Online Hate Speech
English. 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 underresourced languages, data augmentation strategies paired with large unsupervised LMs can held promising results. Italiano. Le Contro Narrative sono risposte testuali volte a contrastare l’odio online e a prevenirne la diffusione. La comunità di NLP ha iniziato a studiare l’uso di architetture neurali per la generazione di CN. Tuttavia, gli sforzi sono stati rivolti esclusivamente all’inglese. In questo lavoro, cerchiamo di colmare la lacuna per l’italiano, mostrando come implementare efficacemente approcci di generazione di CN. Sperimentiamo con un dataset esistente di CN e un modello del linguaggio per l’italiano recentemente rilasciato, in diverse configurazioni, tra cui zero e few shot learning. I risultati mostrano che anche per lingue con poche risorse, strategie di data augmentation abbinate a potenti modelli del linguaggio possono offrire risultati promettenti. Copyright ©2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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