Prottay Kumar Adhikary


2023

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TRAVID: An End-to-End Video Translation Framework
Prottay Kumar Adhikary | Bandaru Sugandhi | Subhojit Ghimire | Santanu Pal | Partha Pakray
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations

2022

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Image Caption Generation for Low-Resource Assamese Language
Prachurya Nath | Prottay Kumar Adhikary | Pankaj Dadure | Partha Pakray | Riyanka Manna | Sivaji Bandyopadhyay
Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)

Image captioning is a prominent Artificial Intelligence (AI) research area that deals with visual recognition and a linguistic description of the image. It is an interdisciplinary field concerning how computers can see and understand digital images& videos, and describe them in a language known to humans. Constructing a meaningful sentence needs both structural and semantic information of the language. This paper highlights the contribution of image caption generation for the Assamese language. The unavailability of an image caption generation system for the Assamese language is an open problem for AI-NLP researchers, and it’s just an early stage of the research. To achieve our defined objective, we have used the encoder-decoder framework, which combines the Convolutional Neural Networks and the Recurrent Neural Networks. The experiment has been tested on Flickr30k and Coco Captions dataset, which have been originally present in the English language. We have translated these datasets into Assamese language using the state-of-the-art Machine Translation (MT) system for our designed work.

2021

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Neural Machine Translation for Tamil–Telugu Pair
Sahinur Rahman Laskar | Bishwaraj Paul | Prottay Kumar Adhikary | Partha Pakray | Sivaji Bandyopadhyay
Proceedings of the Sixth Conference on Machine Translation

The neural machine translation approach has gained popularity in machine translation because of its context analysing ability and its handling of long-term dependency issues. We have participated in the WMT21 shared task of similar language translation on a Tamil-Telugu pair with the team name: CNLP-NITS. In this task, we utilized monolingual data via pre-train word embeddings in transformer model based neural machine translation to tackle the limitation of parallel corpus. Our model has achieved a bilingual evaluation understudy (BLEU) score of 4.05, rank-based intuitive bilingual evaluation score (RIBES) score of 24.80 and translation edit rate (TER) score of 97.24 for both Tamil-to-Telugu and Telugu-to-Tamil translations respectively.