Visual Question Generation in Bengali

• URL: http://arxiv.org/abs/2310.08187v1
• Date: Thu, 12 Oct 2023 10:26:26 GMT
• Title: Visual Question Generation in Bengali
• Authors: Mahmud Hasan, Labiba Islam, Jannatul Ferdous Ruma, Tasmiah Tahsin Mayeesha, Rashedur M. Rahman
• Abstract summary: We develop a novel transformer-based encoder-decoder architecture that generates questions in Bengali when given an image. We establish the first state of the art models for Visual Question Generation task in Bengali. Our results show that our image-cat model achieves a BLUE-1 score of 33.12 and BLEU-3 score of 7.56.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The task of Visual Question Generation (VQG) is to generate human-like questions relevant to the given image. As VQG is an emerging research field, existing works tend to focus only on resource-rich language such as English due to the availability of datasets. In this paper, we propose the first Bengali Visual Question Generation task and develop a novel transformer-based encoder-decoder architecture that generates questions in Bengali when given an image. We propose multiple variants of models - (i) image-only: baseline model of generating questions from images without additional information, (ii) image-category and image-answer-category: guided VQG where we condition the model to generate questions based on the answer and the category of expected question. These models are trained and evaluated on the translated VQAv2.0 dataset. Our quantitative and qualitative results establish the first state of the art models for VQG task in Bengali and demonstrate that our models are capable of generating grammatically correct and relevant questions. Our quantitative results show that our image-cat model achieves a BLUE-1 score of 33.12 and BLEU-3 score of 7.56 which is the highest of the other two variants. We also perform a human evaluation to assess the quality of the generation tasks. Human evaluation suggests that image-cat model is capable of generating goal-driven and attribute-specific questions and also stays relevant to the corresponding image.

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