Sociolinguistically Driven Approaches for Just Natural Language Processing

Natural language processing (NLP) systems are now ubiquitous. Yet the benefits of these language technologies do not accrue evenly to all users, and indeed they can be harmful; NLP systems reproduce stereotypes, prevent speakers of non-standard language varieties from participating fully in public discourse, and re-inscribe historical patterns of linguistic stigmatization and discrimination. How harms arise in NLP systems, and who is harmed by them, can only be understood at the intersection of work on NLP, fairness and justice in machine learning, and the relationships between language and social justice. In this thesis, we propose to address two questions at this intersection: i) How can we conceptualize harms arising from NLP systems?, and ii) How can we quantify such harms? We propose the following contributions. First, we contribute a model in order to collect the first large dataset of African American Language (AAL)-like social media text. We use the dataset to quantify the performance of two types of NLP systems, identifying disparities in model performance between Mainstream U.S. English (MUSE)- and AAL-like text. Turning to the landscape of bias in NLP more broadly, we then provide a critical survey of the emerging literature on bias in NLP and identify its limitations. Drawing on work across sociology, sociolinguistics, linguistic anthropology, social psychology, and education, we provide an account of the relationships between language and injustice, propose a taxonomy of harms arising from NLP systems grounded in those relationships, and propose a set of guiding research questions for work on bias in NLP. Finally, we adapt the measurement modeling framework from the quantitative social sciences to effectively evaluate approaches for quantifying bias in NLP systems. We conclude with a discussion of recent work on bias through the lens of style in NLP, raising a set of normative questions for future work

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Killing me Softly: Creative and Cognitive Aspects of Implicitness in Abusive Language Online

[EN] Abusive language is becoming a problematic issue for our society. The spread of messages that reinforce social and cultural intolerance could have dangerous effects in victims¿ life. State-of-the-art technologies are often effective on detecting explicit forms of abuse, leaving unidentified the utterances with very weak offensive language but a strong hurtful effect. Scholars have advanced theoretical and qualitative observations on specific indirect forms of abusive language that make it hard to be recognized automatically. In this work, we propose a battery of statistical and computational analyses able to support these considerations, with a focus on creative and cognitive aspects of the implicitness, in texts coming from different sources such as social media and news. We experiment with transformers, multi-task learning technique, and a set of linguistic features to reveal the elements involved in the implicit and explicit manifestations of abuses, providing a solid basis for computational applications.Frenda, S.; Patti, V.; Rosso, P. (2022). Killing me Softly: Creative and Cognitive Aspects of Implicitness in Abusive Language Online. Natural Language Engineering. 1-22. https://doi.org/10.1017/S135132492200031612

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Achieving Hate Speech Detection in a Low Resource Setting

Online social networks provide people with convenient platforms to communicate and share life moments. However, because of the anonymous property of these social media platforms, the cases of online hate speeches are increasing. Hate speech is defined by the Cambridge Dictionary as “public speech that expresses hate or encourages violence towards a person or group based on something such as race, religion, sex, or sexual orientation”. Online hate speech has caused serious negative effects to legitimate users, including mental or emotional stress, reputational damage, and fear for one’s safety. To protect legitimate online users, automatically hate speech detection techniques are deployed on various social media. However, most of the existing hate speech detection models require a large amount of labeled data for training. In the thesis, we focus on achieving hate speech detection without using many labeled samples. In particular, we focus on three scenarios of hate speech detection and propose three corresponding approaches. (i) When we only have limited labeled data for one social media platform, we fine-tune a per-trained language model to conduct hate speech detection on the specific platform. (ii) When we have data from several social media platforms, each of which only has a small size of labeled data, we develop a multitask learning model to detect hate speech on several platforms in parallel. (iii) When we aim to conduct hate speech on a new social media platform, where we do not have any labeled data for this platform, we propose to use domain adaptation to transfer knowledge from some other related social media platforms to conduct hate speech detection on the new platform. Empirical studies show that our proposed approaches can achieve good performance on hate speech detection in a low resource setting