SOQAL: Neural Arabic Question Answering

(Under construction) This repository includes the code and dataset described in our WANLP 2019 paper Neural Arabic Question Answering by Hussein Mozannar, Karl El Hajal, Elie Maamary and Hazem Hajj.

Coming soon:

• Trained models and retriever
• Jupyter notebook tutorial for training a simple neural reading comprehension model on the Arabic Reading Comprehension Dataset (ARCD)
• Google Colab for training BERT on Arabic-SQuAD and ARCD

Quick Links:

• Datasets
• BERT
• Document Retrievers
• Getting Arabic Wikipedia
• Tools for Creating our datasets
• Document Reading baselines

Arabic Open Domain Question Answering

This work builds a system for open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. However, this enables to use neural reading comprehension models for our end goal.

Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD) containing 48,344 questions.

Our system for open domain question answering in Arabic (SOQAL) is based on three components: (1) a document retriever using a hierarchical TF-IDF approach, (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT and finally (3) a linear answer ranking module to obtain .

Credit: This work draws inspiration from DrQA.

Platform

Tested for Python 3.6 on Windows 8,10 and Linux. Most commands are written assuming Windows.

Installing SOQAL

(for Windows) Create a new virtual environment (you need to install virtualenv if you want) and activate it:

virtualenv venv
venv\Scripts\activate

Now you are in the virtual environment you have created and will install things here.

Run the following commands to clone the repository and install SOQAL:

git clone https://github.com/husseinmozannar/SOQAL.git
cd SOQAL
pip install -r requirements.txt

Demo

(We will soon provide trained models, this relies on you training BERT and building the retriever)

To interactively ask Arabic open-domain questions to SOQAL, follow the instructions bellow:

python demo_open.py ^
-c bert/multilingual_L-12_H-768_A-12/bert_config.json ^
-v bert/multilingual_L-12_H-768_A-12/vocab.txt ^
-o bert/runs/ ^
-r retriever/tfidfretriever.p

And on your browser go to:

localhost:9999

Citation

(pending ACL release)

Please cite our paper if you use our datasets or code:

@article{mozannar2019neural,
  title={Neural Arabic Question Answering},
  author={Mozannar, Hussein and Hajal, Karl El and Maamary, Elie and Hajj, Hazem},
  journal={arXiv preprint arXiv:1906.05394},
  year={2019}
}