# ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic - English

arXiv: https://arxiv.org/abs/2211.12000 · HTML: https://mlsanity.com/paper/2211.12000/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 73 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 26 of 26)

- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English](https://mlsanity.com/paper/1902.01382/) (2019)
- [Speech Recognition Challenge in the Wild: Arabic MGB-3](https://mlsanity.com/paper/1709.07276/) (2017)
- [Code-Switching for Enhancing NMT with Pre-Specified Translation](https://mlsanity.com/paper/1904.09107/) (2019)
- [Code-switching pre-training for neural machine translation](https://mlsanity.com/paper/2009.08088/) (2020)
- [Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASR](https://mlsanity.com/paper/2105.14779/) (2021)
- [PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation](https://mlsanity.com/paper/2004.09447/) (2020)
- [From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text](https://mlsanity.com/paper/2107.06483/) (2021)
- [HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Text](https://mlsanity.com/paper/2107.03760/) (2021)
- [End-to-End Speech Translation for Code Switched Speech](https://mlsanity.com/paper/2204.05076/) (2022)
- [Can You Traducir This? Machine Translation for Code-Switched Input](https://mlsanity.com/paper/2105.04846/) (2021)
- [Challenges of Computational Processing of Code-Switching](https://mlsanity.com/paper/1610.02213/) (2016)
- [Arabic Code-Switching Speech Recognition using Monolingual Data](https://mlsanity.com/paper/2107.01573/) (2021)
- [A Dataset for Building Code-Mixed Goal Oriented Conversation Systems](https://mlsanity.com/paper/1806.05997/) (2018)
- [Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech](https://mlsanity.com/paper/2108.12881/) (2021)
- [Code-Switching without Switching: Language Agnostic End-to-End Speech Translation](https://mlsanity.com/paper/2210.01512/) (2022)
- [LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual](https://mlsanity.com/paper/1309.5652/) (2013)
- [CALCS 2021 Shared Task: Machine Translation for Code-Switched Data](https://mlsanity.com/paper/2202.09625/) (2022)
- [Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation](https://mlsanity.com/paper/2205.12649/) (2022)
- [Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text](https://mlsanity.com/paper/2210.06990/) (2022)
- [Code-Mixed to Monolingual Translation Framework](https://mlsanity.com/paper/1911.03772/) (2019)
- [Integrating Knowledge in End-to-End Automatic Speech Recognition for Mandarin-English Code-Switching](https://mlsanity.com/paper/2112.10202/) (2021)
- [IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task](https://mlsanity.com/paper/2106.16055/) (2021)

## Built on by (most cited first, 13 of 13)

- [Natural Language Processing for Dialects of a Language: A Survey](https://mlsanity.com/paper/2401.05632/) (2024)
- [TADA: Task-Agnostic Dialect Adapters for English](https://mlsanity.com/paper/2305.16651/) (2023)
- [CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset](https://mlsanity.com/paper/2509.14161/) (2025)
- [ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus](https://mlsanity.com/paper/2403.18182/) (2024)
- [A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2501.13419/) (2025)
- [Towards Real-World Streaming Speech Translation for Code-Switched Speech](https://mlsanity.com/paper/2310.12648/) (2023)
- [Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study](https://mlsanity.com/paper/2310.15262/) (2023)
- [ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech Recognition Using LLMs](https://mlsanity.com/paper/2406.18120/) (2024)
- [DIVERS-Bench: Evaluating Language Identification Across Domain Shifts and Code-Switching](https://mlsanity.com/paper/2509.17768/) (2025)
- [TEDxTN: A Three-way Speech Translation Corpus for Code-Switched Tunisian Arabic - English](https://mlsanity.com/paper/2511.10780/) (2025)
- [ArzEn-MultiGenre: An aligned parallel dataset of Egyptian Arabic song lyrics, novels, and subtitles, with English translations](https://mlsanity.com/paper/2508.01411/) (2025)
- [The Impact of Code-switched Synthetic Data Quality is Task Dependent: Insights from MT and ASR](https://mlsanity.com/paper/2503.23576/) (2025)
- [NADI 2026: The Second Multidialectal Arabic Speech Processing Shared Task](https://mlsanity.com/paper/2609.27086/) (2026)

## Abstract (arXiv)

We present our work on collecting ArzEn-ST, a code-switched Egyptian Arabic - English Speech Translation Corpus. This corpus is an extension of the ArzEn speech corpus, which was collected through informal interviews with bilingual speakers. In this work, we collect translations in both directions, monolingual Egyptian Arabic and monolingual English, forming a three-way speech translation corpus. We make the translation guidelines and corpus publicly available. We also report results for baseline systems for machine translation and speech translation tasks. We believe this is a valuable resource that can motivate and facilitate further research studying the code-switching phenomenon from a linguistic perspective and can be used to train and evaluate NLP systems.