Neural Arabic Question Answering

Hussein Mozannar, Elie Maamary, Karl El Hajal, Hazem Hajj

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.
Anthology ID:
W19-4612
Volume:
Proceedings of the Fourth Arabic Natural Language Processing Workshop
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Wassim El-Hajj, Lamia Hadrich Belguith, Fethi Bougares, Walid Magdy, Imed Zitouni, Nadi Tomeh, Mahmoud El-Haj, Wajdi Zaghouani
Venue:
WANLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
108–118
Language:
URL:
https://aclanthology.org/W19-4612
DOI:
10.18653/v1/W19-4612
Bibkey:
Cite (ACL):
Hussein Mozannar, Elie Maamary, Karl El Hajal, and Hazem Hajj. 2019. Neural Arabic Question Answering. In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 108–118, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Neural Arabic Question Answering (Mozannar et al., WANLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-3/W19-4612.pdf
Code
 husseinmozannar/SOQAL
Data
ARCD, SQuAD, WikiQA
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