Abstract
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.- Anthology ID:
- 2022.acl-long.268
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3868–3888
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.268
- DOI:
- 10.18653/v1/2022.acl-long.268
- Cite (ACL):
- Elena Álvarez-Mellado and Constantine Lignos. 2022. Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3868–3888, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling (Álvarez-Mellado & Lignos, ACL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.acl-long.268.pdf
- Code
- lirondos/coalas