# Overview of ADoBo at IberLEF 2025: Automatic Detection of Anglicisms in Spanish

arXiv: https://arxiv.org/abs/2507.21813 · HTML: https://mlsanity.com/paper/2507.21813/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation](https://mlsanity.com/paper/2005.04322/) (2020)
- [Spanish Pre-trained BERT Model and Evaluation Data](https://mlsanity.com/paper/2308.02976/) (2023)
- [The futility of STILTs for the classification of lexical borrowings in Spanish](https://mlsanity.com/paper/2109.08607/) (2021)
- [Overview of GUA-SPA at IberLEF 2023: Guarani-Spanish Code Switching Analysis](https://mlsanity.com/paper/2309.06163/) (2023)

## Built on by (most cited first, 4 of 4)

- [Are Language Models Borrowing-Blind? A Multilingual Evaluation of Loanword Identification across 10 Languages](https://mlsanity.com/paper/2510.26254/) (2025)
- [Feature-Refined Unsupervised Model for Loanword Detection](https://mlsanity.com/paper/2508.17923/) (2025)
- [Towards a Diagnostic and Predictive Evaluation Methodology for Sequence Labeling Tasks](https://mlsanity.com/paper/2602.12759/) (2026)
- [Do LLMs Know What Luxembourgish Borrows? Probing Lexical Neology in Low-Resource Multilingual Models](https://mlsanity.com/paper/2605.21227/) (2026)

## Abstract (arXiv)

This paper summarizes the main findings of ADoBo 2025, the shared task on anglicism identification in Spanish proposed in the context of IberLEF 2025. Participants of ADoBo 2025 were asked to detect English lexical borrowings (or anglicisms) from a collection of Spanish journalistic texts. Five teams submitted their solutions for the test phase. Proposed systems included LLMs, deep learning models, Transformer-based models and rule-based systems. The results range from F1 scores of 0.17 to 0.99, which showcases the variability in performance different systems can have for this task.