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Found 11 papers, 1 papers with code
IndiText Boost: Text Augmentation for Low Resource India Languages
In this work, we focus on implementing techniques like Easy Data Augmentation, Back Translation, Paraphrasing, Text Generation using LLMs, and Text Expansion using LLMs for text classification on different languages.
Breaking Language Barriers: A Question Answering Dataset for Hindi and Marathi
The recent advances in deep-learning have led to the development of highly sophisticated systems with an unquenchable appetite for data.
Enhancing Low Resource NER Using Assisting Language And Transfer Learning
Named Entity Recognition (NER) is a fundamental task in NLP that is used to locate the key information in text and is primarily applied in conversational and search systems.
Suggesting Relevant Questions for a Query Using Statistical Natural Language Processing Technique
Suggesting similar questions for a user query has many applications ranging from reducing search time of users on e-commerce websites, training of employees in companies to holistic learning for students.
Optimize_Prime@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil
This paper presents the approach used by our team - Optimize_Prime, in the ACL 2022 shared task "Abusive Comment Detection in Tamil."
PICT@DravidianLangTech-ACL2022: Neural Machine Translation On Dravidian Languages
This paper presents a summary of the findings that we obtained based on the shared task on machine translation of Dravidian languages.
Optimize_Prime@DravidianLangTech-ACL2022: Emotion Analysis in Tamil
This paper aims to perform an emotion analysis of social media comments in Tamil.
L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models
Named Entity Recognition (NER) is a basic NLP task and finds major applications in conversational and search systems.
Mono vs Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition
In this work, we consider NER for low-resource Indian languages like Hindi and Marathi.
Analyzing Architectures for Neural Machine Translation Using Low Computational Resources
LSTM performed well in the experiment and took comparatively less time to train than transformers, making it suitable to use in situations having time constraints.
Investigating Transfer Learning Capabilities of Vision Transformers and CNNs by Fine-Tuning a Single Trainable Block
In recent developments in the field of Computer Vision, a rise is seen in the use of transformer-based architectures.
