# Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task

arXiv: https://arxiv.org/abs/1906.04138 · HTML: https://mlsanity.com/paper/1906.04138/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 24 of 24)

- [Are Multilingual Models Effective in Code-Switching?](https://mlsanity.com/paper/2103.13309/) (2021)
- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [Can You Traducir This? Machine Translation for Code-Switched Input](https://mlsanity.com/paper/2105.04846/) (2021)
- [CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languages](https://mlsanity.com/paper/2507.18791/) (2025)
- [CALCS 2021 Shared Task: Machine Translation for Code-Switched Data](https://mlsanity.com/paper/2202.09625/) (2022)
- [El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing](https://mlsanity.com/paper/2101.10524/) (2021)
- [Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data](https://mlsanity.com/paper/2403.12749/) (2024)
- [Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text](https://mlsanity.com/paper/2403.04872/) (2024)
- [HIT: A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation](https://mlsanity.com/paper/2105.14600/) (2021)
- [Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching](https://mlsanity.com/paper/2111.01231/) (2021)
- [Borrowing or Codeswitching? Annotating for Finer-Grained Distinctions in Language Mixing](https://mlsanity.com/paper/2206.04973/) (2022)
- [CST5: Data Augmentation for Code-Switched Semantic Parsing](https://mlsanity.com/paper/2211.07514/) (2022)
- [Overview of ADoBo 2021: Automatic Detection of Unassimilated Borrowings in the Spanish Press](https://mlsanity.com/paper/2110.15682/) (2021)
- [CMNEROne at SemEval-2022 Task 11: Code-Mixed Named Entity Recognition by leveraging multilingual data](https://mlsanity.com/paper/2206.07318/) (2022)
- [Code-mixed Sentiment and Hate-speech Prediction](https://mlsanity.com/paper/2405.12929/) (2024)
- [Low-resource Machine Translation for Code-switched Kazakh-Russian Language Pair](https://mlsanity.com/paper/2503.20007/) (2025)
- [One Source, Two Targets: Challenges and Rewards of Dual Decoding](https://mlsanity.com/paper/2109.10197/) (2021)
- [A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical Transformer](https://mlsanity.com/paper/2204.12753/) (2022)
- [Automatic Identification of Motivation for Code-Switching in Speech Transcripts](https://mlsanity.com/paper/2212.08565/) (2022)
- [The Effect of Alignment Objectives on Code-Switching Translation](https://mlsanity.com/paper/2309.05044/) (2023)
- [Computational Approaches to Arabic-English Code-Switching](https://mlsanity.com/paper/2410.13318/) (2024)
- [PingPong: A Natural Benchmark for Multi-Turn Code-Switching Dialogues](https://mlsanity.com/paper/2601.17277/) (2026)
- [Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition](https://mlsanity.com/paper/2509.02514/) (2025)
- [TurEngMix: A Text Corpus and Benchmark for Turkish-English Code-Mixed Language Identification and Named Entity Recognition](https://mlsanity.com/paper/2609.06963/) (2026)

## Abstract (arXiv)

In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data. We divide the shared task into two competitions based on the English-Spanish (ENG-SPA) and Modern Standard Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity types to establish a new dataset for code-switched NER benchmarks. In addition to the CS phenomenon, the diversity of the entities and the social media challenges make the task considerably hard to process. As a result, the best scores of the competitions are 63.76% and 71.61% for ENG-SPA and MSA-EGY, respectively. We present the scores of 9 participants and discuss the most common challenges among submissions.