README.md

---

license: cc-by-sa-4.0
task_categories:
- token-classification
- translation
language:
- ar
- en
tags:
- code-switching
- arabic
- transliteration
- arabizi
- asr
- asr-post-processing
- dialectal-arabic
- speech-recognition
size_categories:
- 100K<n<1M
configs:
- config_name: default
  data_files:
  - split: train
    path: train.parquet
  - split: validation
    path: validation.parquet
  - split: test
    path: test.parquet
---


# ArabicDeXlit-Corpus

**Training data for undoing transliteration in Arabic ASR output.**

Arabic speech recognition systems handle code-switching badly. When a speaker says an English
word, the model writes it out in Arabic letters — *intern* becomes `انترن`, *AI* becomes `ايه اي`.
The transcript is phonetically sensible and practically unusable: you cannot search it, index it,
or hand it to an English NLP tool.

This corpus trains a model to repair that, **without touching text that is genuinely Arabic**.

| | |
|---|---|
| **Input** (`src`) | `كنت انترن في او اي اي و بعدها جالي اوفر` |
| **Output** (`tgt`) | `كنت intern في OIE و بعدها جالي offer` |
| **Pure Arabic in** | `أنا رايح البيت دلوقتي` |
| **Pure Arabic out** | `أنا رايح البيت دلوقتي` — unchanged |

- **500,346 examples** across **9 dialect labels**
- **29% pass-through rows** where the correct answer is *change nothing*
- **1,417,309 labelled spans**, tagged by category
- Zero train/test leakage, zero token/tag misalignment (both verified — see *Quality guarantees*)

---

## Quick start

```python

from datasets import load_dataset



ds = load_dataset("mohammedaly22/ArabicDeXlit-Corpus")

print(ds)

print(ds["train"][0])

```

Only the sentences that must be left untouched:

```python

passthrough = ds["train"].filter(lambda r: r["is_passthrough"])

```

Only a given dialect:

```python

levantine = ds["train"].filter(lambda r: r["dialect"] == "lev")

```

---

## Row format

```json

{

  "src": "أنا عندي ميتينج مهم",

  "tgt": "أنا عندي meeting مهم",

  "src_tokens": ["أنا", "عندي", "ميتينج", "مهم"],

  "tags": ["O", "O", "B-CS", "O"],

  "spans": [{"start": 2, "end": 3, "category": "CS", "target": "meeting"}],

  "dialect": "egy",

  "source": "sdaia",

  "is_passthrough": false,

  "num_spans": 1

}

```

| Field | Type | Meaning |
|---|---|---|
| `src` | `string` | The noisy ASR-style input — English rendered in Arabic letters. |
| `tgt` | `string` | The corrected output. |
| `src_tokens` | `list[string]` | `src` split on whitespace. |
| `tags` | `list[string]` | **One tag per token of `src`.** BIO scheme; `O` = copy verbatim. |
| `spans` | `list[struct]` | `start`, `end` (exclusive), `category`, `target` — token offsets into `src_tokens`. |
| `dialect` | `string` | Dialect code (see below). |
| `source` | `string` | `sdaia`, `gemini`, `monolingual`, or `sdaia-mono`. |
| `is_passthrough` | `bool` | True when every tag is `O`, i.e. `src == tgt`. |
| `num_spans` | `int32` | Convenience count of `spans`. |

### The tag scheme

`O` plus `B-`/`I-` for each of five categories, 11 tags in total.

`O` is the **identity tag**: a token tagged `O` is copied to the output byte for byte. A sentence
whose tags are all `O` is therefore returned completely unchanged. This is what makes the task
learnable as tagging rather than generation, and it is the property that lets a model built on this
data be safe to drop into a live pipeline.

### Span categories

The category is part of the label because the same characters convert differently depending on it —
`ايه اي` is `AI` as an acronym, but would be `eh ay` as an ordinary word.

| Category | Count | Input | Output |
|---|---|---|---|
| `CS` | 1,395,234 | `انترن` | `intern` |
| `ACRONYM` | 12,393 | `ايه اي` | `AI` |
| `EMAIL` | 4,370 | `احمد ات جيميل دوت كوم` | `ahmed@gmail.com` |
| `ENTITY` | 5,312 | `اورانج انوفيشن ايجيبت` | `Orange Innovation Egypt` |
| `NUMBER` | 0 | `تو زيرو تو فور` | `2024` |

---

## Splits

| Split | Rows | Pass-through | Spans |
|---|---|---|---|
| `train` | 450,338 | 130,321 (28.9%) | 1,275,872 |
| `validation` | 24,865 | 7,160 (28.8%) | 70,182 |
| `test` | 25,143 | 7,229 (28.8%) | 71,255 |

Splits are assigned by **hashing a normalised sentence key** rather than shuffling, so a rebuild
reproduces exactly the same test set — important if this corpus is to serve as a shared benchmark.

## Dialect coverage

| Code | Dialect | Rows |
|---|---|---|
| `unk` | Mixed / unlabelled (monolingual pass-through) | 142,599 |
| `msa` | Modern Standard Arabic | 114,741 |
| `egy` | Egyptian | 111,938 |
| `glf` | Gulf (Saudi/Emirati/Kuwaiti) | 109,522 |
| `lev` | Levantine (Syrian/Lebanese/Jordanian/Palestinian) | 7,823 |
| `mgr` | Maghrebi (Moroccan/Algerian/Tunisian) | 3,935 |
| `yem` | Yemeni | 3,566 |
| `irq` | Iraqi | 3,521 |
| `sdn` | Sudanese | 2,701 |

`unk` is monolingual Arabic contributing pass-through examples; it is dialectally mixed and is
labelled honestly rather than guessed at.

---

## How the data was built

### The core idea: invert the corpus

Parallel data for this task barely exists. But corpora of the **target** form — Arabic with English
correctly in Latin script — are plentiful. So the pipeline runs the problem backwards: given a
target sentence, it transliterates the English into Arabic letters to *manufacture* the noisy ASR
input.

```

target (have it)    كنت intern في Orange

        ↓ transliterate the English into Arabic script

source (make it)    كنت انترن في اورانج

        ↓ record where each rewrite landed

tags                O    B-CS   O   B-CS

```

This matters for correctness, not just convenience. Because spans are recorded **during**
generation rather than recovered afterwards by alignment, the labels cannot silently drift out of
sync with the text — the failure mode that quietly poisons hand-aligned corpora.

### The transliterator is deliberately stochastic

Real ASR is inconsistent. The same English word comes out differently depending on speaker,
accent and acoustics. So each call samples among plausible renderings:

| English | Sampled Arabic forms |
|---|---|
| `intern` | `انترن` · `انتيرن` · `انترين` |
| `meeting` | `ميتينج` · `ميتنج` · `ميتينغ` |
| `project` | `بروجكت` · `برجكت` · `بروجيكت` |

A model trained across several epochs therefore meets the variation it will actually face in
production, instead of one canonical spelling it could memorise.

The generator also models how things are *spoken*: acronyms letter by letter (`AI` → `ايه اي`),
emails symbol by symbol (`@` → `ات`, `.` → `دوت`), and digits as words (`2024` → `تو زيرو تو فور`).

### Sources

| Source | Contribution | License |
|---|---|---|
| [SDAIA ArE-CSTD](https://huggingface.co/datasets/SDAIANCAI/Ar-En-Code-Switching-Textual-Dataset) | 310,578 rows — MSA, Saudi, Egyptian | CC-BY-SA-4.0 |
| Gemini synthesis | 22,584 rows — the missing dialects and rare categories | generated for this corpus |
| Arabic Wikipedia + dialectal ASR transcripts | 144,710 rows — pass-through examples | CC-BY-SA |

**Why synthesis was necessary.** SDAIA covers three dialects and is almost entirely plain
word-level code-switching. Measured on 20K of its sentences: ~60,000 ordinary code-switch spans but
only **178 acronyms, 9 entities, and zero emails or numbers**. Generation was therefore aimed
narrowly at those gaps — the five uncovered dialects, and the acronym / email / number / entity
categories — rather than at bulk volume that SDAIA already supplies.

**Why external monolingual text was necessary.** SDAIA is ~99% code-switched by construction, so it
cannot supply the pass-through half of the task. Those rows come from Arabic Wikipedia and
dialectal ASR transcripts instead.

Crucially, they are **screened**: a "monolingual" sentence containing already-transliterated
English (`الميتينغ`, `الويكند`, `الشوبينج`) is rejected, because using it as a pass-through example
would teach a model to leave transliteration alone — the exact opposite of the task. The screen is
generated by running the project's own transliterator over a list of common loanwords, so it covers
spelling variants without anyone hand-enumerating them.

---

## Quality guarantees

Each of these was **verified on the released build**, not assumed:

| Check | Result |
|---|---|
| Family-key overlap, train ∩ test | **0** |
| Family-key overlap, train ∩ validation | **0** |
| Family-key overlap, validation ∩ test | **0** |
| Rows where `len(tags) != len(src_tokens)` | **0** |
| Tags outside the 11-tag schema | **0** |
| Pass-through rows where `src != tgt` | **0** |

**On leakage specifically.** SDAIA is LLM-generated and contains large families of near-identical
sentences (`اليوم عندي meeting مع ال team في المكتب` and dozens of small variants). A random split
scatters such a family across train and test and inflates every reported score. Here, splitting is
done on a *normalised* key — diacritics stripped, alef/ya/ta-marbuta variants folded, digits
collapsed — so an entire family lands in one split.

One subtle case is handled explicitly: SDAIA writes English *mall* as `مال`, which is also an
ordinary Arabic word, so two different targets can collapse to the same input string. Identical
inputs are pinned to a single split rather than being allowed to follow their differing targets.

---

## Intended uses

- **ASR post-processing** — the motivating use: repair code-switched transcripts in place.
- **Token classification** — detecting transliterated spans (`src_tokens` + `tags`).
- **Character-level transliteration** — Arabic script → Latin, using the `spans` field alone.
- **Evaluating pass-through safety** — `is_passthrough` isolates the rows a model must not alter.

### Limitations, stated plainly

- **`src` is synthetic.** Inputs are generated from a phonetic model of Arabic ASR errors, not
  transcribed from real ASR output. The generator was tuned against real examples and reproduces
  their characteristic forms, but a held-out set of genuine ASR transcripts is the right way to
  confirm any result from this corpus.
- **Dialect balance is uneven.** MSA, Egyptian and Gulf dominate, because that is what the free
  corpora cover; the five synthesised dialects are present in the low thousands each.
- **`ENTITY` and `NUMBER` are thin** relative to `CS`. If your application leans on them, generate
  more with the repository's synthesis script.
- **Some rows come from an LLM** (SDAIA is GPT-4-generated; the dialect top-up is Gemini-generated)
  and carry the register and topic biases that implies.

---

## License

**CC-BY-SA-4.0**, inherited from SDAIA ArE-CSTD's share-alike term. Derivatives must carry the same
licence.

[Casablanca](https://huggingface.co/datasets/UBC-NLP/Casablanca) is deliberately **excluded**. It is
an excellent multi-dialect corpus that annotates code-switched words in both scripts, but it is
licensed CC-BY-**NC-ND** — the no-derivatives term forbids republishing transliterated derivatives
of it. It remains suitable as a held-out evaluation set, cited rather than redistributed.

## Citation

```bibtex

@misc{arabicdexlit2026,

  title  = {ArabicDeXlit: Undoing Transliteration in Arabic ASR Code-Switching},

  author = {Mohammed Aly},

  year   = {2026},

  url    = {https://github.com/MohammedAly22/Arabic-DeXlit}

}

```

Please also cite the underlying corpus:

```bibtex

@inproceedings{ArECSTD2024,

  title     = {Leveraging LLM for Augmenting Textual Data in Code-Switching ASR: Arabic as an Example},

  author    = {Alharbi, Sadeen and Aloraini, Raghad and BinMuqbil, Reem and Ali, Ahmed and

               Bari, Saiful and Alowisheq, Areeb and Alonaizan, Yaser},

  year      = {2024},

  publisher = {NCAI-SDAIA}

}

```

## Links

- **Code & training** — <https://github.com/MohammedAly22/Arabic-DeXlit>
- **Model** — [`mohammedaly22/ArabicDeXlit-base`](https://huggingface.co/mohammedaly22/ArabicDeXlit-base)