# ArQ: Arabic Question Answering Dataset
## Summary
ArQ is a question answering dataset in Levantine Spoken Arabic and Modern Standard Arabic (MSA), consisting of 32,625 triplets (context-question-answer).

## Introduction
The dataset follows the format and methodology of HeQ (<a href="https://github.com/NNLP-IL/Hebrew-Question-Answering-Dataset">Hebrew Questions and Answers Dataset</a>). A team of annotators were given random context paragraphs in either spoken Arabic or MSA, and were asked to write relevant questions and mark the correct answers. The answer to each question was segment of text (span) included in the relevant paragraph.

Paragraphs were sourced using two types of sources: (1) for MSA we used short news articles from an online Israeli-Arabic weekly newspaper, and (2) for spoken Arabic we used transcriptions of short videos and recorded interviews in Levantine Arabic.

**Questions on both sources were written in Levantine Spoken Arabic (no MSA questions were written)**.

## Question Features
__Two types of questions were collected:__

Answerable questions (24,124; 74%): Questions for which a single correct answer is present in the paragraph.

Unanswerable questions (8,501; 26%): Questions related to the paragraph's content, where a correct answer is not present in the paragraph, but the paragraph provides a "plausible" incorrect answer in terms of logic.

## Quality Labels
As part of ongoing quality control during the collection process, and additional checks on the test and validation sets, approximately 12% of the final data was manually chekced for quality.

Triplets received one of the following quality labels:

*Verified*: Questions that passed the threshold and were relatively easy, with wording exactly or similar to the relevant sentence in the paragraph, or very common questions.

*Good*: Questions with wording that was significantly different (lexically or syntactically) from the wording of the relevant sentence in the paragraph.

*Gold*: Questions that involve more complex inference-making.

*Rejected*: Questions that did not pass the threshold and therefore not indcluded in the published data.

## Additional Answers
After splitting the data, the test and validation subsets underwent additional processing. In cases where there were multiple correct answer spans for an answerable question, the additional possible answer spans were added by annotators to both subsets to enhance robustness.

For example, if the answer appears in quotation marks, another possible answer could be the same answer without the quotation marks. Another example involves answers that may or may not include prepositions preceding the content or appositions. Each answerable question in the test and validation sets received 0 to 3 additional possible answers.

## Dataset Statistics
The table below shows the number of answerable and unanswerable questions, by source:

|           | MSA   | Spoken | Total |
|-----------|-------|--------|--------|
| Answerable| 12421 | 11703  | 24124 (74%)  |
| Unanswerable| 4425 | 4076  | 8501 (26%)  |

The table below shows the number of triplets, by sub-set:

|           | MSA   | Spoken | Total |
|-----------|-------|--------|--------|
| Train     | 15080 | 14197  | 29277 (90%)  |
| Val       | 928   | 745    | 1673 (5%) |
| Test      | 838   | 837    | 1675 (5%) |

The table below shows the number of unique questions and paragraphs, by source:

|           | MSA   | Spoken |
|-----------|-------|--------|
| Questions | 16846 (52%) | 15779 (48%)  |
| Unique Paragraphs | 1016 | 1024  |

The table below shows the question word distribution in the dataset:

| What  | Who | How Much/Many | Which | Where | When | How | Why |
|-------|-----|---------------|-------|-------|------|-----|-----|
| 11517 (34%) | 7323 (22%) | 4451 (13%) | 4103 (12%) | 3293 (10%) | 1351 (4%) | 880 (3%) | 700 (2%) |

## Code
upcoming.

## Model
upcoming.

## Contributors
ArQ was annotated by Webiks for MAFAT, as part of <a href="https://github.com/NNLP-IL">NNLP-IL</a>, the Israeli national initiative in the field of NLP in Hebrew and Arabic.

Contributors: Amir Shufaniya (Webiks), Carinne Cherf (Webiks) and Yossy Eizenrouah (MAFAT).