Overview of QuALES at IberLEF 2022: Question Answering Learning from Examples in Spanish

Aiala Rosá, Luis Chiruzzo, Lucía Bouza, Alina Dragonetti, Santiago Castro, Mathías Etcheverry, Santiago Góngora, Santiago Goycoechea, Juan Machado, Guillermo Moncecchi, Juan José Prada, Dina Wonsever

Abstract: We present the results of the QuALES task, which addresses the problem of Extractive Question Answering from texts. For both training and evaluation we use the QuALES corpus, a corpus of Uruguayan media news about the Covid-19 pandemic and related topics. We describe the systems developed by seven participants, all of them based on different BERT-like language models. The best results were obtained using the multilingual RoBERTa model pre-trained with SQUAD-Es-V2, with a fine tuning on the QuALES corpus.