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This work presents a new resource for borrowing identification and analyzesthe performance and errors of several models on this task. We introduce a newannotated corpus of Spanish newswire rich in unassimilated lexical borrowings-- words from one language that are introduced into another withoutorthographic adaptation -- and use it to evaluate how several sequence labelingmodels (CRF, BiLSTM-CRF, and Transformer-based models) perform. The corpuscontains 370,000 tokens and is larger, more borrowing-dense, OOV-rich, andtopic-varied than previous corpora available for this task. Our results showthat a BiLSTM-CRF model fed with subword embeddings along with eitherTransformer-based embeddings pretrained on codeswitched data or a combinationof contextualized word embeddings outperforms results obtained by amultilingual BERT-based model.

Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling