  This work presents a new resource for borrowing identification and analyzes
the performance and errors of several models on this task. We introduce a new
annotated corpus of Spanish newswire rich in unassimilated lexical borrowings
-- words from one language that are introduced into another without
orthographic adaptation -- and use it to evaluate how several sequence labeling
models (CRF, BiLSTM-CRF, and Transformer-based models) perform. The corpus
contains 370,000 tokens and is larger, more borrowing-dense, OOV-rich, and
topic-varied than previous corpora available for this task. Our results show
that a BiLSTM-CRF model fed with subword embeddings along with either
Transformer-based embeddings pretrained on codeswitched data or a combination
of contextualized word embeddings outperforms results obtained by a
multilingual BERT-based model.
