Name
Abstract | ||
|---|---|---|
Machine Comprehension (MC) is a novel task of question answering (QA) discipline. MC tests the ability of the machine to read a text and comprehend its meaning. Deep learning in MC manages to build an end-to-end paradigm based on new neural networks to directly compute the deep semantic matching among question, answers, and the corresponding passage. Deep learning gives state-of-the-art performance results for English MC. The MC problem has not been addressed yet for the Arabic language due to the lack of Arabic MC datasets. This paper presents the first Arabic MC dataset that results from the translation of the SQuAD v1.1 dataset and applying a proposed approach that combines partial translation post-editing and semi-supervised learning. We intend to make this dataset publicly available for the research community. Furthermore, we use the resultant dataset to build an end-to-end deep learning Arabic MC models, which showed promising results. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.5220/0008065402820288 | KDIR: PROCEEDINGS OF THE 11TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL 1: KDIR |
Keywords | Field | DocType |
Arabic Question Answering Systems, Machine Comprehension, Deep Learning, Machine Translation, Post-editing, Semi-supervised Learning | Question answering,Semi-supervised learning,Arabic,Computer science,Machine translation,Artificial intelligence,Deep learning,Artificial neural network,Comprehension,Machine learning,Semantic matching | Conference |
Volume | Citations | PageRank |
2 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmad Eid | 1 | 0 | 0.68 |
| Nagwa M. El-Makky | 2 | 63 | 11.48 |
| Khaled Nagi | 3 | 0 | 0.68 |