Silvia Casola, Simona Frenda, Soda Marem Lo, Erhan Sezerer, Antonio Uva, Valerio Basile, Cristina Bosco, Alessandro Pedrani, Chiara Rubagotti, Viviana Patti, Davide Bernardi. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

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). 2023.

Marco Antonio Stranisci, Rossana Damiano, Enrico Mensa, Viviana Patti, Daniele Radicioni, Tommaso Caselli. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Silvia Casola, Soda Lo, Valerio Basile, Simona Frenda, Alessandra Cignarella, Viviana Patti, Cristina Bosco. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

Abstract 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.

Abstract Abusive language is an important issue in online communication across different platforms and languages. Having a robust model to detect abusive instances automatically is a prominent challenge. Several studies have been proposed to deal with this vital issue by modeling this task in the cross-domain and cross-lingual setting. This paper outlines and describes the current state of this research direction, providing an overview of previous studies, including the available datasets and approaches employed in both cross-domain and cross-lingual settings. This study also outlines several challenges and open problems of this area, providing insights and a useful roadmap for future work.

The European Language Grid enables researchers and practitioners to easily distribute and use NLP resources and models, such as corpora and classifiers. We describe in this paper how, during the course of our EVALITA4ELG project, we have integrated datasets and systems for the Italian language. We show how easy it is to use the integrated systems, and demonstrate in case studies how seamless the application of the platform is, providing Italian NLP for everyone.

Despite the large number of computational resources for emotion recognition, there is a lack of data sets relying on appraisal models. According to Appraisal theories, emotions are the outcome of a multi-dimensional evaluation of events. In this paper, we present APPReddit, the first corpus of non-experimental data annotated according to this theory. After describing its development, we compare our resource with enISEAR, a corpus of events created in an experimental setting and annotated for appraisal. Results show that the two corpora can be mapped notwithstanding different typologies of data and annotations schemes. A SVM model trained on APPReddit predicts four appraisal dimensions without significant loss. Merging both corpora in a single training set increases the prediction of 3 out of 4 dimensions. Such findings pave the way to a better performing classification model for appraisal prediction.

In this article, we investigate the case of human-machine dialogues in the specific domain of commercial customer care. We built a corpus of conversations between users and a customer-care chatbot of an Italian Telecom Company, focusing on a sample of conversations where users contact the service asking for explanations about billing issues or overcharges. We observed that users’ requests are often vague, generic or incomprehensible. In such cases, commercial dialogue systems typically ask for clarifications or further details to fully understand users’ specific requests. However, from the corpus analysis it appeared that chatbot's clarifying requests may result in ineffective interactions, with users eventually giving up the conversation or switching to a human agent for a faster query resolution. A recovery strategy is thus needed to anticipate users’ information needs, or intentions. We address this issue resorting to GEN-DS, a dialogue system based on symbolic data-to-text generation. GEN-DS analyzes the user-company contextual relational knowledge, with the aim to generate more relevant answers to unclear questions. In this article, we describe the GEN-DS architecture along with the experiments we carried out to evaluate its output. Results from an offline human evaluation show significant improvements of GEN-DS compared to the original system. These improvements concern properties such as utility, necessity, understandability, and quickness of the information communicated in the dialogue. We believe that GEN-DS techniques may find application in all the dialogue systems that need to manage vague requests and must rely on relational knowledge.

In today’s media and in the Web of Data, non-Western people still suffer a lack of representation. In our work, we address this issue by presenting a pipeline for collecting and semantically encoding Wikipedia biographies of writers who are under-represented due to their non-Western origins, or their legal status in a country. The two main components of the ontology will be described, together with a framework for mapping textual biographies to their corresponding semantic representations. A description of the data set, and some examples of biographical texts conversion to the Ontology Classes, will be provided.

Sentiment analysis in social media is a popular task attracting the interest of the research community, also in recent evaluation campaigns of natural language processing tasks in several languages. We report on our experience in the organization of SENTIment POLarity Classification Task (SENTIPOLC), a shared task on sentiment classification of Italian tweets, proposed for the first time in 2014 within the Evalita evaluation campaign. We present the datasets-which include an enriched annotation scheme for dealing with the impact of figurative language on polarity-the evaluation methodology, and discuss the approaches and results of participating systems. We also offer a reflection on the open challenges of state-of-the-art systems for sentiment analysis of microblogging in Italian, as they emerge from a qualitative analysis of misclassified tweets. Finally, we provide an evaluation of the resources we have created, and share the lessons learned by running this task for two consecutive editions.

In this paper we propose an approach to exploit the fine-grained knowledge expressed by individual human annotators during a hate speech (HS) detection task, before the aggregation of single judgments in a gold standard dataset eliminates non-majority perspectives. We automatically divide the annotators into groups, aiming at grouping them by similar personal characteristics (ethnicity, social background, culture etc.). To serve a multi-lingual perspective, we performed classification experiments on three different Twitter datasets in English and Italian languages. We created different gold standards, one for each group, and trained a state-of-the-art deep learning model on them, showing that supervised models informed by different perspectives on the target phenomena outperform a baseline represented by models trained on fully aggregated data. Finally, we implemented an ensemble approach that combines the single perspective-aware classifiers into an inclusive model. The results show that this strategy further improves the classification performance, especially with a significant boost in the recall of HS prediction.

Swearing plays an ubiquitous role in everyday conversations among humans, both in oral and textual communication, and occurs frequently in social media texts, typically featured by informal language and spontaneous writing. Such occurrences can be linked to an abusive context, when they contribute to the expression of hatred and to the abusive effect, causing harm and offense. However, swearing is multifaceted and is often used in casual contexts, also with positive social functions. In this study, we explore the phenomenon of swearing in Twitter conversations, taking the possibility of predicting the abusiveness of a swear word in a tweet context as the main investigation perspective. We developed the Twitter English corpus SWAD (Swear Words Abusiveness Dataset), where abusive swearing is manually annotated at the word level. Our collection consists of 1,511 unique swear words from 1,320 tweets. We developed models to automatically predict abusive swearing, to provide an intrinsic evaluation of SWAD and confirm the robustness of the resource. We also present the results of a glass box ablation study in order to investigate which lexical, syntactic, and affective features are more informative towards the automatic prediction of the function of swearing.

International audience

The Social Web represents nowadays the principal means to support and foster social interactions among people through Web 2.0 technologies. Individuals interact in virtual communities to pursue mutual interests or goals, by exchanging multiple kinds of contents (i.e., textual, acoustic, visual), the so-called User-Generated Content (UGC). In this context, the SeCredISData Special Session is especially devoted at discussing the implications that the analysis of big social data has in tackling open issues related to society from different perspectives. On one side, there is the need to push forward the research on emotion and sentiment, and the investigation of affective cognitive models and their possible integration into intelligent systems. On the other side, it is urgent to address the issue of on-line information credibility assessment, in an era where trusted intermediaries have disappeared and people must rely only on their cognitive capacities to judge information. The Special Session is therefore aimed at promoting the development of models and applications able to tackle these issues.

Jihen Karoui, Farah Benamara, Véronique Moriceau, Viviana Patti, Cristina Bosco, Nathalie Aussenac-Gilles. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. 2017.

[EN] Today¿s Internet is evolving toward an open society of humans and computational\nentities, where intelligent agent systems increasingly support the interaction between\nusers and computational components. In this scenario, affect plays a key role, with functions\nthat span from creating and maintaining interpersonal relations, to establishing\ncooperation and trust. Artificial systems¿which can act as both actors and facilitators\nof these interactions¿are more and more requested to integrate affective components\nin order to achieve truly realistic social behaviors and foster the creation of bonds with\nthe users, with timely reactions to their affective input and appropriate expressions\nof affect. Achieving this integration requires to understand and reproduce the role of\naffect in human expressive capabilities, and to account for affect-related phenomena\n(e.g., sentiment, emotions, and mood) that engage social abilities, such as empathy,\nand expressive means, such as irony. The expectation for complex phenomena such as\nempathy and irony is that effective approaches require an interdisciplinary approach\nand, above all, the integration of representational models and data-oriented processing\ntechniques. The aim of this special section is to bring together leading research\non computational models of affect-related phenomena in interactions occurring either\nin social media or agent-based systems, by attaining cross-fertilization between two\nrelevant perspectives: on the one hand, research on agent architectures and cognitive models, mainly concerned with the integration of affective states into agents and open\nto the creation of virtual and embodied agents; on the other hand, research on techniques\nfor sentiment analysis and opinion mining, mainly focused on the processing\nof affective information in social media, typically (but not only) expressed through\ntext. The integration of models and methods between the two perspectives can open\nthe way to the development of a new generation of social and interactive applications\nthat leverage the affective dimension to promote improved, spontaneous technologymediated\ninteractions, including human¿computer and human¿human interactions,\non small and large scale.\nOur goal here is to provide an overview of the open research challenges for the\ncommunity of researchers interested in analyzing and modeling the interplay between\naffect and interaction in agent-based systems and social media, as well as an introduction\nto the special section. The rest of the article is structured as follows. Section 2\ndiscusses a set of research challenges relevant in the context of this special issue,\nSection 3 briefly introduces the articles included in this ACM TOIT special section.\nSection 4 concludes the article.

Irony has been proven to be pervasive in social media, posing a challenge to sentiment analysis systems. It is a creative linguistic phenomenon where affect-related aspects play a key role. In this work, we address the problem of detecting irony in tweets, casting it as a classification problem. We propose a novel model that explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. Classification experiments over different corpora show that affective information helps in distinguishing among ironic and nonironic tweets. Our model outperforms the state of the art in almost all cases.

Political debates about a reform may sparkle national controversies, by leading members of the community to polarize their opinions and sentiment about the topic addressed. With the rise of social media like Twitter users are encouraged to voice and share their strong and polarized views and in general people are exposed to broader viewpoints than they were before. The large amount of user-generated social data available is a great opportunity to investigate the communicative behaviors emerging in the context of such political debates and to shed some light on the way communities of users with different roles in the society and different political sentiment interact. In this paper we focussed on communications in Twitter around the reform of marriage in France in 2012 and 2013 - “Le Mariage Pour Tous” - which had been the subject of debate and controversy. We collected a corpus of tweets tagged by the hashtag #mariagepourtous, created to mark the messages about the reform. We applied different kinds of analysis on our dataset based on linguistic and non linguistic features of the observed data in order to investigate the communicative behavior in using subjective and evaluative language on a political topic. The analysis leaded also to reflect on the impact of different typologies of users involved in the virtual debate which included both political messages created by media organizations and by other individual users, from ordinary citizens to politicians or celebrities.

Interaction protocols play a fundamental role in multiagent systems. In this work, after analyzing the trends that are emerging not only from research on multiagent interaction protocols but also from neighboring fields, like research on workflows and business processes, we propose a novel definition of commitment-based interaction protocols, that is characterized by the decoupling of the constitutive and the regulative specifications and that explicitly foresees a representation of the latter based on constraints among commitments. A clear distinction between the two representations has many advantages, mainly residing in a greater openness of multiagent systems, and an easier reuse of protocols and of action definitions. A language, named 2CL, for writing regulative specifications is also given together with a designer-oriented graphical notation.