Abstract
AbstractUrdu is morphologically rich language and lacks the resources available in English. While several studies on the image captioning task in English have been published, this is among the pioneer studies on Urdu generative image captioning. The study makes several key contributions: (i) it presents a new dataset for Urdu image captioning, and (ii) it presents different attention-based architectures for image captioning in the Urdu language. These attention mechanisms are new to the Urdu language, as those have never been used for the Urdu image captioning task (iii) Finally, it performs quantitative and qualitative analysis of the results by studying the impact of different model architectures on Urdu’s image caption generation task. The extensive experiments on the Urdu image caption generation task show encouraging results such as a BLEU-1 score of 72.5, BLEU-2 of 56.9, BLEU-3 of 42.8, and BLEU-4 of 31.6. Finally, we present data and code used in the study for future research via GitHub (https://github.com/saeedhas/Urdu_cap_gen).
Publisher
Springer Science and Business Media LLC
Reference44 articles.
1. Agrawal H, Desai K, Wang Y, Chen X, Jain R, Johnson M, Batra D, Parikh D, Lee S, Anderson P (2019) Nocaps: novel object captioning at scale. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 8948–8957
2. Amjad M, Sidorov G, Zhila A (2020) Data augmentation using machine translation for fake news detection in the urdu language. In: Proceedings of the 12th language resources and evaluation conference, LREC 2020, Marseille, France, May 11-16, 2020. European Language Resources Association, pp. 2537–2542
3. Aneja J, Deshpande A, Schwing AG (2018) Convolutional image captioning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5561–5570
4. Artetxe M, Schwenk H (2019) Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond. Transac Assoc Comput Linguist 7:597–610
5. Bahdanau D, Cho KH, Bengio Y (2015) Neural machine translation by jointly learning to align and translate.” In: 3rd International Conference on Learning Representations
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