2022/03/30 by Elena Álvarez Mellado, Álvarez-Mellado, Elena, Constantine Lignos +1 · 2 citations
Arts and Humanities · Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Linguistics, Language Diversity, and Identity #Natural Language Processing Techniques #Text Readability and Simplification
paper · pdf · doi:10.48550/arxiv.2203.16169
openalex publication_date 2022/03/30 · openalex created_date 2022/04/26 · openalex updated_date 2026/07/28
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.