---
title: "ArzEn-ST — paper by Hamed et al. (2022) — summary and ideas"
description: "This paper introduces ArzEn-ST, a speech translation resource for Egyptian Arabic mixed with English. 69 references, cited by 14 papers here."
authors: ["Injy Hamed", "Nizar Habash", "Slim Abdennadher", "Ngoc Thang Vu"]
year: 2022
venue: "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)"
source: "https://aclanthology.org/2022.wanlp-1.12/"
doi: "10.18653/v1/2022.wanlp-1.12"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/hamed-2022-arzen-st-three-way-speech/"
---
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# ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic-English

**Authors:** [Injy Hamed](https://md.research.elamur.ai/free/en/authors/injy-hamed/), [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/), [Slim Abdennadher](https://md.research.elamur.ai/free/en/authors/slim-abdennadher/) and [Ngoc Thang Vu](https://md.research.elamur.ai/free/en/authors/ngoc-thang-vu/) · **Year:** 2022 · **Venue:** [Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/) ([WANLP 2022](https://md.research.elamur.ai/free/en/venues/wanlp/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.wanlp-1.12/) · [PDF](https://aclanthology.org/2022.wanlp-1.12.pdf) · [DOI 10.18653/v1/2022.wanlp-1.12](https://doi.org/10.18653/v1/2022.wanlp-1.12) · [OpenAlex](https://openalex.org/W4385573226)

**Citations:** 12 citations (OpenAlex, 2026-10-14) · [14](https://md.research.elamur.ai/free/en/papers/hamed-2022-arzen-st-three-way-speech/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Code-switching, where speakers mix languages within speech, is common in multilingual communities. NLP systems still handle it poorly, mostly because there is little data. This paper introduces ArzEn-ST, a speech translation resource for Egyptian Arabic mixed with English. It builds on the ArzEn corpus, which contains about 12 hours of informal interviews with bilingual university students and staff in Cairo. The authors add human translations of each utterance in two directions: fully Egyptian Arabic and fully English. Each utterance therefore has four aligned views: audio, transcript, Arabic version and English version. They publish the corpus, the translation guidelines, data splits and scripts.

They also train baseline systems:
- ASR;
- Transformer MT with Fairseq;
- a cascaded speech translation pipeline.

Each baseline is trained on ArzEn-ST alone and also with large external corpora. Extra data helps a great deal. Translating into Arabic scores far higher than translating into English, and the speech pipeline loses roughly half the MT quality. Because the mixed speech is spontaneous rather than read or commissioned, the resource fills a real gap for training and testing.

## Key points
- The source is the ArzEn corpus: 38 bilingual speakers aged 18–35, 12 hours of speech and 6,216 sentences, recorded at the German University in Cairo with no instructions about switching.
- The corpus is three-way parallel: each code-switched utterance has a monolingual Egyptian Arabic translation and a monolingual English translation.
- The authors describe four standard switching types (inter-sentential, extra-sentential, intra-sentential, intra-word) and add two repetition-based types: explicatory and elaboratory.
- MT setup: Fairseq Transformers with FLORES low-resource hyperparameters and a joint BPE vocabulary, tuned between 1k and 16k. The extra training data is 324k Egyptian Arabic–English pairs from Callhome, several LDC sets and MADAR. When the target is monolingual Arabic, the 15k code-switched pairs are removed from this extra data.
- Test results, with figures before → after adding extra data:

| Task | Metric | ArzEn-ST only | With extra data |
|---|---|---|---|
| ASR | WER | 57.9 | 34.7 |
| ASR | CER | 36.2 | 20.0 |
| MT, mixed → English | BLEU | 8.6 | 34.3 |
| MT, mixed → Arabic | BLEU | 48.0 | 79.8 |
| Cascaded ST, mixed → English | BLEU | 4.5 | 16.5 |
| Cascaded ST, mixed → Arabic | BLEU | 13.0 | 31.1 |

## Interesting ideas
- Translating naturally produced mixed utterances into both of their languages yields aligned monolingual "views" of one utterance, which can later reveal why a switch happened at a given point.
- The authors argue that synthetic data is acceptable for training but should never serve as a test set, because it does not match real-world distributions.
- Repetitions are classified by communicative purpose: plain restatement in another language (explicatory) versus restatement that adds detail (elaboratory).
- Training pairs whose target side does not match the reference style (code-switched Arabic) are filtered out when the goal is monolingual output.
- Results are reported both in-domain only and with external data, and the drop from MT to the cascaded ST pipeline is shown explicitly.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (back): Paired English paraphrases of the same input could test whether text → graph → text keeps meaning stable across different surface forms.
- **I2** (learn): Keep evaluation sets for silver-trained parsers strictly natural, human-checked text, and treat generated sentences as training material only.
- **I3** (world): A restated phrase should map onto the existing entity and enrich it rather than create a duplicate, which helps keep entity counts and holdings correct.
- **I4** (learn): Filter treebank or silver data whose annotation dialect differs from Mova's UD conventions instead of mixing it in blindly.
- **I5** (answer): Score each pipeline stage separately and with or without extra knowledge, so the stage where answer quality collapses can be located.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Stuttgart](https://md.research.elamur.ai/free/en/universities/university-of-stuttgart/) — University, Stuttgart, Germany · 94 works · 46,609 citations
- [New York University Abu Dhabi](https://md.research.elamur.ai/free/en/universities/new-york-university-abu-dhabi/) — University, Abu Dhabi, United Arab Emirates · 124 works · 4,709 citations
- [German University in Cairo](https://md.research.elamur.ai/free/en/universities/german-university-in-cairo/) — University, Cairo, Egypt · 6 works · 74 citations

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## Cite

Cite as: Injy Hamed, Nizar Habash, Slim Abdennadher and Ngoc Thang Vu. 2022. ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic-English. Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{hamed2022arzen,
  title = {{ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic-English}},
  author = {Injy Hamed, Nizar Habash, Slim Abdennadher and Ngoc Thang Vu},
  booktitle = {Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)},
  year = {2022},
  doi = {10.18653/v1/2022.wanlp-1.12},
  url = {https://doi.org/10.18653/v1/2022.wanlp-1.12}
}
```

- [← previous A Dependency Treebank of Spoken Second Language English](https://md.research.elamur.ai/free/en/papers/kyle-2022-dependency-treebank-spoken-second-language/)
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- [↑ back to papers of 2022](https://md.research.elamur.ai/free/en/papers/2022/page/6/)

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*Updated 2 November 2026*