publications

2024

1. SEPLN 2024

   Overview of DETESTS-Dis at IberLEF 2024: DETEction and classification of racial STereotypes in Spanish-Learning with Disagreement

   Wolfgang S Schmeisser-Nieto, Pol Pastells, Simona Frenda, Alejandro Ariza-Casabona, Mireia Farrús, Paolo Rosso, and Mariona Taulé

   Procesamiento del Lenguaje Natural, 2024

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   This paper presents an overview of the DETESTS-Dis shared task as part of the IberLEF 2024 Workshop on Iberian Languages Evaluation Forum, within the framework of the SEPLN 2024 conference. We proposed two hierarchical subtasks: In subtask 1, participants had to determine the presence of stereotypes in the texts. For subtask 2, participants had to decide which texts labeled with stereotypes were implicit stereotypes. The DETESTS-Dis dataset contains 12,111 comment sentences and tweets in response to newspaper articles and verified racial hoaxes involving immigration in Spanish. 15 teams signed up to participate, 6 of which sent runs, and 3 of them sent their working notes. In this paper, we provide information about the training and test datasets, the systems used by the participants, the evaluation metrics of the systems and their results.

2. SEPLN 2024 (Poster)

   Context-Aware Stereotype Detection: Conversational Thread Analysis on BERT-based Models

   Pol Pastells, Wolfgang S Schmeisser-Nieto, Simona Frenda, and Mariona Taulé

   CEUR Proceedings SEPLN, 2024

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   Conversational context plays a pivotal role in disambiguating messages in human communication. In this study, we investigate the impact of contextual information on detecting stereotypes related to immigrants using various BERT-based models. We use two Spanish corpora containing news comments and tweets, together with their conversational threads, annotated with stereotypes related to immigrants in Spain. The results show that the influence of context on stereotype detection varies across different models, corpora and context levels. Although context can enhance performance in specific scenarios, it does not consistently improve stereotype detection across all the levels of contexts. Our comprehensive evaluation underscores the complex relationship between context and stereotype identification when we use BERT-based Language Models. In particular, we found that the number of texts benefiting from contextual analysis may be too limited for the models to effectively learn from.

3. LREC-COLING 2024

   

   Human vs. Machine Perceptions on Immigration Stereotypes

   Wolfgang S. Schmeisser-Nieto, Pol Pastells, Simona Frenda, and Mariona Taule

   In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) , May 2024

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   The increasing popularity of natural language processing has led to a race to improve machine learning models that often leaves aside the core study object, the language itself. In this study, we present classification models designed to detect stereotypes related to immigrants, along with both quantitative and qualitative analyses, shedding light on linguistic distinctions in how humans and various models perceive stereotypes. Given the subjective nature of this task, one of the models incorporates the judgments of all annotators by utilizing soft labels. Through a comparative analysis of BERT-based models using both hard and soft labels, along with predictions from GPT-4, we gain a clearer understanding of the linguistic challenges posed by texts containing stereotypes. Our dataset comprises Spanish Twitter posts collected as responses to immigrant-related hoaxes, annotated with binary values indicating the presence of stereotypes, implicitness, and the requirement for conversational context to understand the stereotype. Our findings suggest that both model prediction confidence and inter-annotator agreement are higher for explicit stereotypes, while stereotypes conveyed through irony and other figures of speech prove more challenging to detect than other implicit stereotypes.

4. Nuclear Fusion

   

   Modeling of frequency-sweeping Alfvén modes in the TJ-II stellarator

   AG Ghiozzi, MJ Mantsinen, Pol Pastells, DA Spong, AV Melnikov, LG Eliseev, and SE Sharapov

   Nuclear Fusion, May 2024

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   Alfvénic activity has been observed in the TJ-II stellarator which resembles the frequency sweeping demonstrated by Alfvén cascade modes in tokamaks. A numerical validation study was conducted using a reduced magnetohydrodynamic (MHD) model to show that such modes could only have been observed in discharges where the rotational transform profile was non-monotonic. During experiments, coil current was varied which resulted in shifting of the minimum value of the rotational transform profile. To mimic this effect, we study the Alfvénic activity predicted by the reduced MHD model for a set of input rotational transform profiles with varying minima. A mode is found whose toroidal and poloidal mode numbers match those predicted in experiments which sweeps downward/upward in frequency as the minimum value of the rotational transform profile is increased/decreased. The results serve as a demonstration of the validity and utility of MHD spectroscopy.

5. Scientific Reports

   

   ePyDGGA: automatic configuration for fitting epidemic curves

   Josep Alòs, Carlos Ansótegui, Ivan Dotu, Manuel Garcı́a-Herranz, Pol Pastells, and Eduard Torres

   Scientific Reports, May 2024

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   Many epidemiological models and algorithms are used to fit the parameters of a given epidemic curve. On many occasions, fitting algorithms are interleaved with the actual epidemic models, which yields combinations of model-parameters that are hard to compare among themselves. Here, we provide a model-agnostic framework for epidemic parameter fitting that can (fairly) compare different epidemic models without jeopardizing the quality of the fitted parameters. Briefly, we have developed a Python framework that expects a Python function (epidemic model) and epidemic data and performs parameter fitting using automatic configuration. Our framework is capable of fitting parameters for any type of epidemic model, as long as it is provided as a Python function (or even in a different programming language). Moreover, we provide the code for different types of models, as well as the implementation of 4 concrete models with data to fit them. Documentation, code and examples can be found at https://ulog.udl.cat/static/doc/epidemic-gga/html/index.html.