Work

Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation

Year: 2019

Type: conference-paper

Abstract: In this work, we present several deep learning models for the automatic diacritization of Arabic text.Our models are built using two main approaches, viz.Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF) and Block-Normalized Gradient (BNG).The models are tested on the only freely available benchmark dataset and the results show that our models are either better or on par with other models, which require language-dependent postprocessing steps, unlike ours.Moreover, we show that diacritics in Arabic can be used to enhance the models of NLP tasks such as Machine Translation (MT) by proposing the Translation over Diacritization (ToD) approach.

Source: Workshop on Asian Translation (WAT)

DOI: 10.18653/v1/d19-5229

Authors: Ali Fadel, Ibraheem Tuffaha, Bara’ Al-Jawarneh, Mahmoud Al-Ayyoub

Institution: Jordan University of Science and Technology

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Nvidia, Jordan University of Science and Technology

Award: G7073471076