@InProceedings{adhikary-EtAl:2023:ijcnlp, author = {Adhikary, Prottay Kumar and Sugandhi, Bandaru and Ghimire, Subhojit and Pal, Santanu and Pakray, Partha}, title = {TRAVID: An End-to-End Video Translation Framework}, booktitle = {System Demonstrations}, month = {November}, year = {2023}, address = {Bali, Indonesia}, publisher = {Asian Federation of Natural Language Processing}, pages = {1--9} }

@inproceedings{vetagiri-etal-2023-cnlp, title = "{CNLP}-{NITS} at {S}em{E}val-2023 Task 10: Online sexism prediction, {PREDHATE}!", author = "Vetagiri, Advaitha and Adhikary, Prottay and Pakray, Partha and Das, Amitava", booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.semeval-1.113", doi = "10.18653/v1/2023.semeval-1.113", pages = "815--822", abstract = "Online sexism is a rising issue that threatens women{'}s safety, fosters hostile situations, and upholds social inequities. We describe a task SemEval-2023 Task 10 for creating English-language models that can precisely identify and categorize sexist content on internet forums and social platforms like Gab and Reddit as well to provide an explainability in order to address this problem. The problem is divided into three hierarchically organized subtasks: binary sexism detection, sexism by category, and sexism by fine-grained vector. The dataset consists of 20,000 labelled entries. For Task A, pertained models like Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), which is called CNN-BiLSTM and Generative Pretrained Transformer 2 (GPT-2) models were used, as well as the GPT-2 model for Task B and C, and have provided experimental configurations. According to our findings, the GPT-2 model performs better than the CNN-BiLSTM model for Task A, while GPT-2 is highly accurate for Tasks B and C on the training, validation and testing splits of the training data provided in the task. Our proposed models allow researchers to create more precise and understandable models for identifying and categorizing sexist content in online forums, thereby empowering users and moderators.", }

@article{vetagiri2023leveraging, title={Leveraging GPT-2 for automated classification of online sexist content}, author={Vetagiri, Advaitha and Adhikary, Prottay Kumar and Pakray, Partha and Das, Amitava}, year={2023} }

@article{ADHIKARY20232350, title = {Dzongkha Handwritten Digit Recognition using Machine Learning Techniques}, journal = {Procedia Computer Science}, volume = {218}, pages = {2350-2358}, year = {2023}, note = {International Conference on Machine Learning and Data Engineering}, issn = {1877-0509}, doi = {https://doi.org/10.1016/j.procs.2023.01.210}, url = {https://www.sciencedirect.com/science/article/pii/S1877050923002107}, author = {Prottay Kumar Adhikary and Pankaj Dadure and Pradipta Saha and Tawmo and Partha Pakray}, keywords = {Dzongkha Language, Character Recognition, Digit Recognition, Handwritten Characters, Machine Learning}, abstract = {Handwritten digit recognition has recently gained importance, attracting many researchers due to its use in various machine learning and computer vision applications. As technology and science progressing, there is a need for a system to recognize the handwritten script in several real-time applications to reduce human effort. There a lot of work has been done on the recognition and generation of handwritten digits of high-resource languages such as English. However, insufficient work has been done on Dzongkha digits recognition, as Dzongkha digits are low-resource and more complex than English patterns. This paper aims to perform handwritten character recognition of Dzongkha digit using several machine learning techniques. The unavailability of the Dzongkha handwritten digit dataset is the prime motivation behind this work. To facilitates the recognition of Dzongkha handwritten digit, we have collected the data of Dzongkha handwritten digit from indigenous and non-indigenous people of Bhutan and provided the dataset for further research. Moreover, we have used several machine algorithms, including a support vector machine, K-nearest neighbor, and decision tree. Among these algorithms, the support vector machine classification algorithm has achieved a remarkable result with an accuracy of 98.29%.} }

@Article{Laskar2022, author={Laskar, Sahinur Rahman and Gogoi, Abinash and Dutta, Samudranil and Adhikary, Prottay Kumar and Nath, Prachurya and Pakray, Partha and Bandyopadhyay, Sivaji}, title={Investigation of negation effect for English--Assamese machine translation}, journal={S{\={a}}dhan{\={a}}}, year={2022}, month={Nov}, day={14}, volume={47}, number={4}, pages={238}, abstract={Computational linguistics deals with the computational modelling of natural languages, in which machine translation is a popular task. The aim of machine translation is to automatically translate one natural language into another, which minimizes the linguistic barrier of different linguistic backgrounds. The data-driven approach of machine translation, namely, neural machine translation achieves state-of-the-art results on different language pairs, however it needs a sufficient amount of parallel training data to attain reasonable translation performance. In this work, we have explored different machine translation models on a low-resource English--Assamese language pair and investigated different sources of errors, particularly due to negation in English-to-Assamese and Assamese-to-English translation. Negation is a universal, essential feature of human language that has a substantial impact on the semantics of a statement. Moreover, a rule-based approach is proposed in the data preprocessing step which handles modal-verb negation problem that shows significant improvement in translation performance in terms of automatic and manual evaluation scores.}, issn={0973-7677}, doi={10.1007/s12046-022-01965-5}, url={https://doi.org/10.1007/s12046-022-01965-5} }

@inproceedings{nath-etal-2022-image, title = "Image Caption Generation for Low-Resource {A}ssamese Language", author = "Nath, Prachurya and Adhikary, Prottay Kumar and Dadure, Pankaj and Pakray, Partha and Manna, Riyanka and Bandyopadhyay, Sivaji", booktitle = "Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)", month = nov, year = "2022", address = "Taipei, Taiwan", publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)", url = "https://aclanthology.org/2022.rocling-1.33", pages = "263--272", abstract = "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.", }

@InProceedings{10.1007/978-3-031-10766-5_26, author="Adhikary, Prottay Kumar and Manna, Riyanka and Laskar, Sahinur Rahman and Pakray, Partha", editor="Mukhopadhyay, Somnath and Sarkar, Sunita and Dutta, Paramartha and Mandal, Jyotsna Kumar and Roy, Sudipta", title="Ontology-Based Healthcare Hierarchy Towards Chatbot", booktitle="Computational Intelligence in Communications and Business Analytics", year="2022", publisher="Springer International Publishing", address="Cham", pages="326--335", abstract="Ontology refers to relationship-based hierarchical descriptions of concepts within a particular domain. Ontology, in the field of medicine, describes the concepts of medical terminologies and the relation between them, thus, enabling the sharing of medical knowledge. This paper aims to develop an ontology-based healthcare hierarchy and point out the research scope towards the chatbot application. The research scope includes the integration of the ontology-based healthcare hierarchy in the chatbot application by the establishment of relationships among individuals and real-world entities.", isbn="978-3-031-10766-5" }

@InProceedings{10.1007/978-3-031-10766-5_22, author="Tawmo and Adhikary, Prottay Kumar and Dadure, Pankaj and Pakray, Partha", editor="Mukhopadhyay, Somnath and Sarkar, Sunita and Dutta, Paramartha and Mandal, Jyotsna Kumar and Roy, Sudipta", title="An Empirical Analysis on Abstractive Text Summarization", booktitle="Computational Intelligence in Communications and Business Analytics", year="2022", publisher="Springer International Publishing", address="Cham", pages="280--287", abstract="With the massive growth of blogs, news stories, and reports, extracting useful information from such a large quantity of textual documents has become a difficult task. Automatic text summarization is an excellent approach for summarising these documents. Text summarization aims to condense large documents into concise summaries while preserving essential information and meaning. A variety of fascinating summarising models have been developed to achieve state-of-the-art performance in terms of fluency, human readability, and semantically meaningful summaries. In this paper, we have investigated the OpenNMT tool for task text summarization. The OpenNMT is the encoder-decoder-based neural machine translation model which has been fine-tuned for the task of abstractive text summarization. The proposed OpenNMT based text summarization approach has been tested on freely available dataset such as CNNDM {\&} MSMO dataset and depicts their proficiency in terms of ROUGE and BLEU score.", isbn="978-3-031-10766-5" }

@dataset{tawmo_2022_6271560, author = {Tawmo and Prottay Kumar Adhikary and Pankaj Dadure and Partha Pakray}, title = {Dzongkha Handwritten Digit Dataset}, month = feb, year = 2022, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.6271560}, url = {https://doi.org/10.5281/zenodo.6271560} }

@inproceedings{laskar-etal-2021-neural, title = "Neural Machine Translation for {T}amil{--}{T}elugu Pair", author = "Laskar, Sahinur Rahman and Paul, Bishwaraj and Adhikary, Prottay Kumar and Pakray, Partha and Bandyopadhyay, Sivaji", booktitle = "Proceedings of the Sixth Conference on Machine Translation", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.wmt-1.29", pages = "284--287", abstract = "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.", }