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The first edition of the IberLEF 2021 shared task on automatic detection ofborrowings (ADoBo) focused on detecting lexical borrowings that appeared in theSpanish press and that have recently been imported into the Spanish language.In this work, we tested supplementary training on intermediate labeled-datatasks (STILTs) from part of speech (POS), named entity recognition (NER),code-switching, and language identification approaches to the classification ofborrowings at the token level using existing pre-trained transformer-basedlanguage models. Our extensive experimental results suggest that STILTs do notprovide any improvement over direct fine-tuning of multilingual models.However, multilingual models trained on small subsets of languages performreasonably better than multilingual BERT but not as good as multilingualRoBERTa for the given dataset.

The futility of STILTs for the classification of lexical borrowings in Spanish