# Overview of ADoBo 2021: Automatic Detection of Unassimilated Borrowings in the Spanish Press

arXiv: https://arxiv.org/abs/2110.15682 · HTML: https://mlsanity.com/paper/2110.15682/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks](https://mlsanity.com/paper/1811.01088/) (2018)
- [Overview for the Second Shared Task on Language Identification in Code-Switched Data](https://mlsanity.com/paper/1909.13016/) (2019)
- [Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task](https://mlsanity.com/paper/1906.04138/) (2019)
- [The futility of STILTs for the classification of lexical borrowings in Spanish](https://mlsanity.com/paper/2109.08607/) (2021)

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

- [Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling](https://mlsanity.com/paper/2203.16169/) (2022)
- [IberBench: LLM Evaluation on Iberian Languages](https://mlsanity.com/paper/2504.16921/) (2025)
- [Do LLMs Know What Luxembourgish Borrows? Probing Lexical Neology in Low-Resource Multilingual Models](https://mlsanity.com/paper/2605.21227/) (2026)
- [pylazaro: a Python package for anglicism extraction in Spanish](https://mlsanity.com/paper/2609.29276/) (2026)

## Abstract (arXiv)

This paper summarizes the main findings of the ADoBo 2021 shared task, proposed in the context of IberLef 2021. In this task, we invited participants to detect lexical borrowings (coming mostly from English) in Spanish newswire texts. This task was framed as a sequence classification problem using BIO encoding. We provided participants with an annotated corpus of lexical borrowings which we split into training, development and test splits. We received submissions from 4 teams with 9 different system runs overall. The results, which range from F1 scores of 37 to 85, suggest that this is a challenging task, especially when out-of-domain or OOV words are considered, and that traditional methods informed with lexicographic information would benefit from taking advantage of current NLP trends.