Adversarial Question Answering in Spanish with Transformer Models

Abstract

In this work, a system for adversarial Question Answering (QA) in Spanish is presented. Models BETO, MarIA-base, MarIA-large and RigoBERTa are tried, although finally only last 3 are used. They are first trained over a big adversarial QA corpus, AllQA. AllQA is composed of SQUAD-ES v2, a translated version of NewsQA presented in this work, and QUALES. These general QA models are then retrained over QUALES dataset. Finally, their predictions are aggregated via meta-ensembling techniques, to produce more reliable answers to the presented questions. Results in terms of F1-score are presented on the validation set of AllQA and QUALES, and complete official results on the test set of QUALES in terms of F1-score and exact match are also presented.

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Adversarial Question Answering in Spanish with Transformer Models.