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Vasu Jindal

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2 papers

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AAAI Conference 2018 Short Paper

Generating Image Captions in Arabic Using Root-Word Based Recurrent Neural Networks and Deep Neural Networks

Vasu Jindal

Automatic caption generation of an image requires both computer vision and natural language processing techniques. Despite of advanced research in English caption generation, research on generating Arabic descriptions of an image is extremely limited. Semitic languages like Arabic are heavily influenced by root-words. We leverage this critical dependency of Arabic and in this paper are the first to generate captions of an image directly in Arabic using root-word based Recurrent Neural Networks and Deep Neural Networks. We report the first BLEU score for direct Arabic caption generation. Experimental results confirm that generating image captions using root-words directly in Arabic significantly outperforms the English-Arabic translated captions using state-of-the-art methods.

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AAAI Conference 2017 Short Paper

A Deep Learning Approach for Arabic Caption Generation Using Roots-Words

Vasu Jindal

Automatic caption generation is a key research field in the machine learning community. However, most of the current research is performed on English caption generation ignoring other languages like Arabic and Persian. In this paper, we propose a novel technique leveraging the heavy influence of root words in Arabic to automatically generate captions in Arabic. Fragments of the images are associated with root words and deep belief network pre-trained using Restricted Boltzmann Machines are used to extract words associated with image. Finally, dependency tree relations are used to generate sentence-captions by using the dependency on root words. Our approach is robust and attains BLEU-1 score of 34.8.

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