Soda Marem Lo


2024

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From Hate Speech to Societal Empowerment: A Pedagogical Journey Through Computational Thinking and NLP for High School Students
Alessandra Teresa Cignarella | Elisa Chierchiello | Chiara Ferrando | Simona Frenda | Soda Marem Lo | Andrea Marra
Proceedings of the Sixth Workshop on Teaching NLP

The teaching laboratory we have created integrates methodologies to address the topic of hate speech on social media among students while fostering computational thinking and AI education for societal impact. We provide a foundational understanding of hate speech and introduce computational concepts using matrices, bag of words, and practical exercises in platforms like Colaboratory. Additionally, we emphasize the application of AI, particularly in NLP, to address real-world challenges. Through retrospective evaluation, we assess the efficacy of our approach, aiming to empower students as proactive contributors to societal betterment. With this paper we present an overview of the laboratory’s structure, the primary materials used, and insights gleaned from six editions conducted to the present date.

2023

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Confidence-based Ensembling of Perspective-aware Models
Silvia Casola | Soda Marem Lo | Valerio Basile | Simona Frenda | Alessandra Cignarella | Viviana Patti | Cristina Bosco
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Research in the field of NLP has recently focused on the variability that people show in selecting labels when performing an annotation task. Exploiting disagreements in annotations has been shown to offer advantages for accurate modelling and fair evaluation. In this paper, we propose a strongly perspectivist model for supervised classification of natural language utterances. Our approach combines the predictions of several perspective-aware models using key information of their individual confidence to capture the subjectivity encoded in the annotation of linguistic phenomena. We validate our method through experiments on two case studies, irony and hate speech detection, in in-domain and cross-domain settings. The results show that confidence-based ensembling of perspective-aware models seems beneficial for classification performance in all scenarios. In addition, we demonstrate the effectiveness of our method with automatically extracted perspectives from annotations when the annotators’ metadata are not available.

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EPIC: Multi-Perspective Annotation of a Corpus of Irony
Simona Frenda | Alessandro Pedrani | Valerio Basile | Soda Marem Lo | Alessandra Teresa Cignarella | Raffaella Panizzon | Cristina Marco | Bianca Scarlini | Viviana Patti | Cristina Bosco | Davide Bernardi
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present EPIC (English Perspectivist Irony Corpus), the first annotated corpus for irony analysis based on the principles of data perspectivism. The corpus contains short conversations from social media in five regional varieties of English, and it is annotated by contributors from five countries corresponding to those varieties. We analyse the resource along the perspectives induced by the diversity of the annotators, in terms of origin, age, and gender, and the relationship between these dimensions, irony, and the topics of conversation. We validate EPIC by creating perspective-aware models that encode the perspectives of annotators grouped according to their demographic characteristics. Firstly, the performance of perspectivist models confirms that different annotators induce very different models. Secondly, in the classification of ironic and non-ironic texts, perspectivist models prove to be generally more confident than the non-perspectivist ones. Furthermore, comparing the performance on a perspective-based test set with those achieved on a gold standard test set, we can observe how perspectivist models tend to detect more precisely the positive class, showing their ability to capture the different perceptions of irony. Thanks to these models, we are moreover able to show interesting insights about the variation in the perception of irony by the different groups of annotators, such as among different generations and nationalities.