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.
ABSTRACT
The Task on Semantic Analysis at SEPLN (TASS task within IberLEF 2020 workshop) took place on September 22, reaching its ninth edition. Due to the COVID-19 pandemic, the number of participants is lower compared to past campaigns. Also, the organizers decided to held it remotely. In this edition, the classical polarity classification subtask was, again, organized. As a novelty, a second subtask was proposed to foster research in emotion detection of Spanish texts on a new dataset. This paper summarizes the different approaches of the teams who participated, the key insights of their systems and the results obtained for all the proposed solutions. © 2020 Copyright for this paper by its authors. Use permitted under.