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https://github.com/husseinmozannar/SOQAL

Arabic Open Domain Question Answering System using Neural Reading Comprehension
https://github.com/husseinmozannar/SOQAL

arabic arabic-language arabic-nlp deep-learning nlp question-answering reading-comprehension tf-idf

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Arabic Open Domain Question Answering System using Neural Reading Comprehension

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README 

        
# SOQAL: Neural Arabic Question Answering

This repository includes the code and dataset described in our [WANLP 2019 paper Neural Arabic Question Answering](https://arxiv.org/abs/1906.05394) by Hussein Mozannar, Karl El Hajal, Elie Maamary and Hazem Hajj.



*  See below how to run a demo of our open domain question answering system in Arabic



*  Google Colab for training BERT on Arabic-SQuAD and ARCD: [Colab](https://colab.research.google.com/drive/19a_jIKpjhQez0KTa_Qwh2BW2nryGXzhb)



Quick Links:

*  [Datasets](data/README.md)

*  [BERT](bert/README.md)

*  [Document Retrievers](retriever/README.md)

*  [Getting Arabic Wikipedia](arwiki/README.md)

*  [Tools for Creating our datasets](dataset_creation/README.md)

*  [Document Reading baselines](baselines_reading/README.md)

## Arabic Open Domain Question Answering

![](system_fig.jpg)

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](https://github.com/facebookresearch/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:

```shell

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:

```shell

git clone https://github.com/husseinmozannar/SOQAL.git

cd SOQAL

pip install -r requirements.txt

```



## Demo

After installing the required packages, we have to download trained retriever and reader.



First BERT model: [cased multilingual](https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip), trained reader [trained BERT](https://storage.googleapis.com/soqal_traind_models/mod.zip) and checkpoint file [checkpoint](https://storage.googleapis.com/soqal_traind_models/checkpoint): export the mod.zip and place the checkpoint file inside the mod folder, retriever [retriever](https://storage.googleapis.com/soqal_traind_models/tfidfretriever.p).



Export BERT model and place in bert/, export trained reader and place it in bert/ and place tfretriever.p in retriever/



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



```shell

python demo_open.py ^

-c bert/multi_cased_L-12_H-768_A-12/bert_config.json ^

-v bert/multi_cased_L-12_H-768_A-12/vocab.txt ^

-o bert/mod/ ^

-r retriever/tfidfretriever.p

```



And on your browser go to:

```

localhost:9999

```

## Citation



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



```

@inproceedings{mozannar-etal-2019-neural,

    title = "Neural {A}rabic Question Answering",

    author = "Mozannar, Hussein  and

      Maamary, Elie  and

      El Hajal, Karl  and

      Hajj, Hazem",

    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",

    month = aug,

    year = "2019",

    address = "Florence, Italy",

    publisher = "Association for Computational Linguistics",

    url = "https://www.aclweb.org/anthology/W19-4612",

    doi = "10.18653/v1/W19-4612",

    pages = "108--118",

    abstract = "This paper tackles the problem of 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. 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). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.",

}

```