Datasets:

ali-issa
/

TAAD

---
license: cc-by-4.0
task_categories:
- text-classification
language:
- ar
size_categories:
- n<1K
tags:
- arabic
- diacritics
- tashkeel
- minimal-pairs
- disambiguation
- morphology
- evaluation
configs:
- config_name: default
  data_files: taad.parquet
---

# TAAD: Teacher-Approved Arabic Diacritics

A small benchmark of 110 Arabic minimal pairs in which **the diacritics alone
decide the meaning**. Each item is one sentence written two ways. Strip the
short vowel marks and the two spellings become the identical string, so a model
that cannot read the marks has no information with which to choose.

## What is in it

Each item gives two vocalizations of the same consonantal skeleton, and one
short continuation that fits each reading:

| | Arabic | reading |
|---|---|---|
| A | أُحِبُّ الشِّعْرَ → العَرَبِيَّ | *I love Arabic poetry* |
| B | أُحِبُّ الشَّعْرَ → الطَّوِيلَ | *I love long hair* |

Both sentences reduce to **أحب الشعر** once diacritics are removed.

The intended use is a forced choice: given one reading of the sentence, is its
own continuation scored above the other one? Each item is asked in both
directions, giving 220 decisions in total, so a model that simply prefers one
continuation wins once and loses once and gains nothing.

## Construction constraints

Every item satisfies all of the following, checked mechanically:

1. The two sentences are identical once diacritics are stripped.
2. Shadda (U+0651) is identical in both readings. The stripping convention
   preserves shadda, so a pair differing in it would be visible to a
   diacritic-blind model.
3. Exactly one word differs between the readings, and it is the last word.
4. **Both continuations carry the same final short vowel.** This is the most
   important constraint. Without it, a model can succeed by copying the case
   vowel of the preceding word rather than by reading the ambiguous word. In an
   earlier version of this design that lacked the constraint, the copying
   shortcut alone resolved every position in the set.
5. The two continuations are different words, never one adjective in two cases.
6. One word per continuation.
7. The contrast is lexical, not a case contrast: the two forms differ inside
   the word, not only in a final inflectional vowel.

## Provenance

Items were written and reviewed by an Arabic language teacher. Rows marked
`authored` were composed by the reviewer; rows marked `validated` were drafted
as candidates against the constraints above and then checked and approved by
the same reviewer. All 110 passed review.

Candidates that failed any construction constraint were discarded before
review.

## Dataset structure

| field | type | description |
|---|---|---|
| `id` | string | Stable item identifier, `taad-001` … `taad-110` |
| `sentence_a` | string | Reading A, fully diacritized |
| `ending_a` | string | The one-word continuation that fits reading A |
| `sentence_b` | string | Reading B, fully diacritized |
| `ending_b` | string | The one-word continuation that fits reading B |
| `word_pair` | string | The ambiguous word, undiacritized |
| `provenance` | string | `authored` or `validated` |
| `undiacritized` | string | The shared consonantal string both readings reduce to |

110 rows, one configuration, no splits.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("ali-issa/TAAD", split="train")
item = ds[0]

# the two forced choices for this item
#   given sentence_a: is ending_a scored above ending_b?
#   given sentence_b: is ending_b scored above ending_a?
print(item["sentence_a"], "->", item["ending_a"])
print(item["sentence_b"], "->", item["ending_b"])
print(item["undiacritized"])   # identical for both readings
```

### Suggested scoring

Tokenize `sentence + " " + continuation` as a single string, run one forward
pass, and sum the log-probability of the continuation's tokens only. The
sentence is identical across the two continuations and would otherwise dominate
the comparison.

Because the two continuations are different words, a raw sum favours the
shorter one. Dividing by the character length of the **undiacritized
continuation** gives a divisor that is a property of the item and identical for
every model, so it cannot favour one tokenizer over another. Dividing by a
model's own token count does not have this property and is not comparable
across tokenizers.

A graded alternative that needs no normalization at all:

```
displacement = [logP(end_a | sent_a) − logP(end_b | sent_a)]
             − [logP(end_a | sent_b) − logP(end_b | sent_b)]
```

Each continuation appears once with each sign, so its length, frequency and
token count cancel exactly. What remains reflects only the change between the
two vocalizations. Zero means the diacritics changed nothing.

### A note on the expected floor

A model whose vocabulary cannot encode short vowels maps both readings of every
item to the identical token sequence. Its two scores are then equal by
construction and it scores exactly chance with exactly zero displacement. This
is arithmetic rather than a measurement of ability, and it is the point of the
set: these sentences are unresolvable without the marks as a matter of what is
written.

When reporting confidence intervals, resample **items** rather than decisions.
The two directions of an item are strongly anti-correlated by construction and
are not independent observations.

## Citation

```bibtex
@misc{taad2026,
  title  = {{TAAD}: Teacher-Approved Arabic Diacritics},
  author = {{Author names withheld during review}},
  year   = {2026},
  publisher = {Hugging Face},
  note   = {Author list and URL will be added once the accompanying paper is published}
}
```

## License

CC BY 4.0.

## Authors

Author names withheld during review.

## Contact

Contact details will be added once the accompanying paper is published.