Franco M. Luque

Juan Manuel Pérez, Franco Luque, Demian Zayat et al.

In recent years, hate speech has gained great relevance in social networks and other virtual media because of its intensity and its relationship with violent acts against members of protected groups. Due to the great amount of content generated by users, great effort has been made in the research and development of automatic tools to aid the analysis and moderation of this speech, at least in its most threatening forms. One of the limitations of current approaches to automatic hate speech detection is the lack of context. Most studies and resources are performed on data without context; that is, isolated messages without any type of conversational context or the topic being discussed. This restricts the available information to define if a post on a social network is hateful or not. In this work, we provide a novel corpus for contextualized hate speech detection based on user responses to news posts from media outlets on Twitter. This corpus was collected in the Rioplatense dialectal variety of Spanish and focuses on hate speech associated with the COVID-19 pandemic. Classification experiments using state-of-the-art techniques show evidence that adding contextual information improves hate speech detection performance for two proposed tasks (binary and multi-label prediction). We make our code, models, and corpus available for further research.

Juan Manuel Pérez, Mariela Rajngewerc, Juan Carlos Giudici et al.

In recent years, the extraction of opinions and information from user-generated text has attracted a lot of interest, largely due to the unprecedented volume of content in Social Media. However, social researchers face some issues in adopting cutting-edge tools for these tasks, as they are usually behind commercial APIs, unavailable for other languages than English, or very complex to use for non-experts. To address these issues, we present pysentimiento, a comprehensive multilingual Python toolkit designed for opinion mining and other Social NLP tasks. This open-source library brings state-of-the-art models for Spanish, English, Italian, and Portuguese in an easy-to-use Python library, allowing researchers to leverage these techniques. We present a comprehensive assessment of performance for several pre-trained language models across a variety of tasks, languages, and datasets, including an evaluation of fairness in the results.

Juan Manuel Pérez, Damián A. Furman, Laura Alonso Alemany et al.

Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for Natural Language Understanding tasks. Recently, some works geared towards pre-training specially-crafted models for particular domains, such as scientific papers, medical documents, user-generated texts, among others. These domain-specific models have been shown to improve performance significantly in most tasks. However, for languages other than English such models are not widely available. In this work, we present RoBERTuito, a pre-trained language model for user-generated text in Spanish, trained on over 500 million tweets. Experiments on a benchmark of tasks involving user-generated text showed that RoBERTuito outperformed other pre-trained language models in Spanish. In addition to this, our model achieves top results for some English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and has also competitive performance against monolingual models in English tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.

Juan Manuel Pérez, Aymé Arango, Franco Luque

This paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive Language Detection. We jointly-trained a single model by fine-tuning Multilingual BERT to tackle the task across all the proposed languages: English, Danish, Turkish, Greek and Arabic. Our single model had competitive results, with a performance close to top-performing systems in spite of sharing the same parameters across all languages. Zero-shot and few-shot experiments were also conducted to analyze the transference performance among these languages. We make our code public for further research

Franco M. Luque

In this article we describe our participation in TASS 2019, a shared task aimed at the detection of sentiment polarity of Spanish tweets. We combined different representations such as bag-of-words, bag-of-characters, and tweet embeddings. In particular, we trained robust subword-aware word embeddings and computed tweet representations using a weighted-averaging strategy. We also used two data augmentation techniques to deal with data scarcity: two-way translation augmentation, and instance crossover augmentation, a novel technique that generates new instances by combining halves of tweets. In experiments, we trained linear classifiers and ensemble models, obtaining highly competitive results despite the simplicity of our approaches.

Franco M. Luque

Formal languages theory is useful for the study of natural language. In particular, it is of interest to study the adequacy of the grammatical formalisms to express syntactic phenomena present in natural language. First, it helps to draw hypothesis about the nature and complexity of the speaker-hearer linguistic competence, a fundamental question in linguistics and other cognitive sciences. Moreover, from an engineering point of view, it allows the knowledge of practical limitations of applications based on those formalisms. In this article I introduce the adequacy problem of grammatical formalisms for natural language, also introducing some formal language theory concepts required for this discussion. Then, I review the formalisms that have been proposed in history, and the arguments that have been given to support or reject their adequacy. ----- La teoría de lenguajes formales es útil para el estudio de los lenguajes naturales. En particular, resulta de interés estudiar la adecuación de los formalismos gramaticales para expresar los fenómenos sintácticos presentes en el lenguaje natural. Primero, ayuda a trazar hipótesis acerca de la naturaleza y complejidad de las competencias lingüísticas de los hablantes-oyentes del lenguaje, un interrogante fundamental de la lingüística y otras ciencias cognitivas. Además, desde el punto de vista de la ingeniería, permite conocer limitaciones prácticas de las aplicaciones basadas en dichos formalismos. En este artículo hago una introducción al problema de la adecuación de los formalismos gramaticales para el lenguaje natural, introduciendo también algunos conceptos de la teoría de lenguajes formales necesarios para esta discusión. Luego, hago un repaso de los formalismos que han sido propuestos a lo largo de la historia, y de los argumentos que se han dado para sostener o refutar su adecuación.