Hiba Arnaout, Anmol Goel, H. Andrew Schwartz, Steffen T. Eberhardt, Dana Atzil-Slonim, Gavin Doherty, Brian Schwartz, Wolfgang Lutz, Tim Althoff, Munmun De Choudhury, Hamidreza Jamalabadi, Raj Sanjay Shah, Flor Miriam Plaza-del-Arco, Dirk Hovy, Maria Liakata, Iryna Gurevych. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Emergent communication models support interaction-based language learning, benefiting both Natural Language Processing (NLP) applications and simulations of language evolution, but they are prone to destabilizing language drift.Inspired by developmental trajectories in human language acquisition, this paper investigates whether age-based plasticity, where younger agents learn quickly and older agents maintain stable representations, can reduce language drift.In our set-up, static populations first reliably develop shared languages, followed by a phase in which population turnover gradually replaces older agents with new learners.Age-based plasticity significantly reduces drift in this setting, maintaining high accuracy and language similarity.In contrast, in populations with uniformly low plasticity agents cannot adapt quickly enough to integrate newcomers and in those with uniformly high plasticity the language changes faster than stable conventions can form.These findings demonstrate that developmental trajectories in individual learners substantially reduce overall language drift in dynamic populations.

María Grandury, Javier Aula-Blasco, Júlia Falcão, Clémentine Fourrier, Miguel González Saiz, Gonzalo Martínez, Gonzalo Santamaria Gomez, Rodrigo Agerri, Nuria Aldama García, Luis Chiruzzo, Javier Conde, Helena Gomez Adorno, Marta Guerrero Nieto, Guido Ivetta, Natàlia López Fuertes, Flor Miriam Plaza-del-Arco, María-Teresa Martín-Valdivia, Helena Montoro Zamorano, Carmen Muñoz Sanz, Pedro Reviriego, Leire Rosado Plaza, Alejandro Vaca Serrano, Estrella Vallecillo-Rodríguez, Jorge Vallego, Irune Zubiaga. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Justin Zhao, Flor Miriam Plaza-del-Arco, Benjamin Genchel, Amanda Cercas Curry. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

The presence of offensive comments on social media has become a major issue in the use of the Internet. Natural Language Processing area has been studying methods and tools for the automatic detection of such content in social networks. The MeOffendEs task in the IberLEF evaluation campaign opened this problem to the research community for the Spanish language. This paper studies two methods for integrating certain contextual information with several transformer-based models. Our findings suggest that the contextual information provided by organizers does not contribute to a better prediction power. Instead, hyper-parameter search is a very important step in the general learning process, leading to systems outperforming the state-of-the-art in Spanish offensiveness detection. • Two methods for embedding contextual data to identify offensive content are tested. • Contextual information has no significant effect compared to Hyper-parameter search. • Hyper-parameter tuning beats state-of-the-art results in Spanish offensive detection.

Flor Miriam Plaza-del-Arco, Amanda Cercas Curry, Alba Curry, Gavin Abercrombie, Dirk Hovy. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in natural language processing (NLP). However, there is no consensus on scope, direction, or methods. In this paper, we conduct a thorough review of 154 relevant NLP publications from the last decade. Based on this review, we address four different questions: (1) How are EA tasks defined in NLP? (2) What are the most prominent emotion frameworks and which emotions are modeled? (3) Is the subjectivity of emotions considered in terms of demographics and cultural factors? and (4) What are the primary NLP applications for EA? We take stock of trends in EA and tasks, emotion frameworks used, existing datasets, methods, and applications. We then discuss four lacunae: (1) the absence of demographic and cultural aspects does not account for the variation in how emotions are perceived, but instead assumes they are universally experienced in the same manner; (2) the poor fit of emotion categories from the two main emotion theories to the task; (3) the lack of standardized EA terminology hinders gap identification, comparison, and future goals; and (4) the absence of interdisciplinary research isolates EA from insights in other fields. Our work will enable more focused research into EA and a more holistic approach to modeling emotions in NLP.

With mental health issues on the rise on the Web, especially among young people, there is a growing need for effective identification and intervention. In this paper, we introduce a new open-sourced corpus for the early detection of mental disorders in Spanish, focusing on eating disorders, depression, and anxiety. It consists of user messages posted on groups within the Telegram message platform and contains over 1,300 subjects with more than 45,000 messages posted in different public Telegram groups. This corpus has been manually annotated via crowdsourcing and is prepared for its use in several Natural Language Processing tasks including text classification and regression tasks. The samples in the corpus include both text and time data. To provide a benchmark for future research, we conduct experiments on text classification and regression by using state-of-the-art transformer-based models.

Recent instruction fine-tuned models can solve multiple NLP tasks when prompted to do so, with machine translation (MT) being a prominent use case.However, current research often focuses on standard performance benchmarks, leaving compelling fairness and ethical considerations behind.In MT, this might lead to misgendered translations, resulting, among other harms, in the perpetuation of stereotypes and prejudices.In this work, we address this gap by investigating whether and to what extent such models exhibit gender bias in machine translation and how we can mitigate it.Concretely, we compute established gender bias metrics on the WinoMT corpus from English to German and Spanish.We discover that IFT models default to male-inflected translations, even disregarding female occupational stereotypes.Next, using interpretability methods, we unveil that models systematically overlook the pronoun indicating the gender of a target occupation in misgendered translations.Finally, based on this finding, we propose an easy-to-implement and effective bias mitigation solution based on fewshot learning that leads to significantly fairer translations.1

Within textual emotion classification, the set of relevant labels depends on the domain and application scenario and might not be known at the time of model development. This conflicts with the classical paradigm of supervised learning in which the labels need to be predefined. A solution to obtain a model with a flexible set of labels is to use the paradigm of zero-shot learning as a natural language inference task, which in addition adds the advantage of not needing any labeled training data. This raises the question how to prompt a natural language inference model for zero-shot learning emotion classification. Options for prompt formulations include the emotion name anger alone or the statement “This text expresses anger”. With this paper, we analyze how sensitive a natural language inference-based zero-shot-learning classifier is to such changes to the prompt under consideration of the corpus: How carefully does the prompt need to be selected? We perform experiments on an established set of emotion datasets presenting different language registers according to different sources (tweets, events, blogs) with three natural language inference models and show that indeed the choice of a particular prompt formulation needs to fit to the corpus. We show that this challenge can be tackled with combinations of multiple prompts. Such ensemble is more robust across corpora than individual prompts and shows nearly the same performance as the individual best prompt for a particular corpus.

In this paper we present SHARE, a new lexical resource with 10,125 offensive terms and expressions collected from Spanish speakers. We retrieve this vocabulary using an existing chatbot developed to engage a conversation with users and collect insults via Telegram, named Fiero. This vocabulary has been manually labeled by five annotators obtaining a kappa coefficient agreement of 78.8%. In addition, we leverage the lexicon to release the first corpus in Spanish for offensive span identification research named OffendES_spans. Finally, we show the utility of our resource as an interpretability tool to explain why a comment may be considered offensive.

In recent years emotion detection in text has become more popular due to its potential applications in fields such as psychology, marketing, political science, and artificial intelligence, among others. While opinion mining is a well-established task with many standard data sets and well-defined methodologies, emotion mining has received less attention due to its complexity. In particular, the annotated gold standard resources available are not enough. In order to address this shortage, we present a multilingual emotion data set based on different events that took place in April 2019. We collected tweets from the Twitter platform. Then one of seven emotions, six Ekman’s basic emotions plus the “neutral or other emotions”, was labeled on each tweet by 3 Amazon MTurkers. A total of 8,409 in Spanish and 7,303 in English were labeled. In addition, each tweet was also labeled as offensive or no offensive. We report some linguistic statistics about the data set in order to observe the difference between English and Spanish speakers when they express emotions related to the same events. Moreover, in order to validate the effectiveness of the data set, we also propose a machine learning approach for automatically detecting emotions in tweets for both languages, English and Spanish.

Today, misogyny and xenophobia are some of the most important social problems. With the increase in the use of social media, this feeling of hatred toward women and immigrants can be more easily expressed, and therefore it can have harmful effects on social media users. For this reason, it is important to develop systems capable of detecting hateful comments automatically. In this article, we analyze the hate speech in Spanish tweets against women and immigrants conducting classification experiments using different approaches. Moreover, we create appropriate language resources for hate speech detection in Spanish.