Amulya Ratna Dash

Also published as: Amulya Ratna Dash


2024

pdf bib
Empowering Low-Resource Language Translation: Methodologies for Bhojpuri-Hindi and Marathi-Hindi ASR and MT
Harpreet Singh Anand | Amulya Ratna Dash | Yashvardhan Sharma
Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)

The paper describes our submission for the unconstrained track of ‘Dialectal and Low-Resource Task’ proposed in IWSLT-2024. We designed cascaded Speech Translation systems for the language pairs Marathi-Hindi and Bhojpuri-Hindi utilising and fine-tuning different pre-trained models for carrying out Automatic Speech Recognition (ASR) and Machine Translation (MT).

2022

pdf bib
BITS Pilani at HinglishEval: Quality Evaluation for Code-Mixed Hinglish Text Using Transformers
Shaz Furniturewala | Vijay Kumari | Amulya Ratna Dash | Hriday Kedia | Yashvardhan Sharma
Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges

Code-Mixed text data consists of sentences having words or phrases from more than one language. Most multi-lingual communities worldwide communicate using multiple languages, with English usually one of them. Hinglish is a Code-Mixed text composed of Hindi and English but written in Roman script. This paper aims to determine the factors influencing the quality of Code-Mixed text data generated by the system. For the HinglishEval task, the proposed model uses multilingual BERT to find the similarity between synthetically generated and human-generated sentences to predict the quality of synthetically generated Hinglish sentences.

2021

pdf bib
NLPHut’s Participation at WAT2021
Shantipriya Parida | Subhadarshi Panda | Ketan Kotwal | Amulya Ratna Dash | Satya Ranjan Dash | Yashvardhan Sharma | Petr Motlicek | Ondřej Bojar
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

This paper provides the description of shared tasks to the WAT 2021 by our team “NLPHut”. We have participated in the English→Hindi Multimodal translation task, English→Malayalam Multimodal translation task, and Indic Multi-lingual translation task. We have used the state-of-the-art Transformer model with language tags in different settings for the translation task and proposed a novel “region-specific” caption generation approach using a combination of image CNN and LSTM for the Hindi and Malayalam image captioning. Our submission tops in English→Malayalam Multimodal translation task (text-only translation, and Malayalam caption), and ranks second-best in English→Hindi Multimodal translation task (text-only translation, and Hindi caption). Our submissions have also performed well in the Indic Multilingual translation tasks.

2020

pdf bib
ODIANLP’s Participation in WAT2020
Shantipriya Parida | Petr Motlicek | Amulya Ratna Dash | Satya Ranjan Dash | Debasish Kumar Mallick | Satya Prakash Biswal | Priyanka Pattnaik | Biranchi Narayan Nayak | Ondřej Bojar
Proceedings of the 7th Workshop on Asian Translation

This paper describes the ODIANLP submission to WAT 2020. We have participated in the English-Hindi Multimodal task and Indic task. We have used the state-of-the-art Transformer model for the translation task and InceptionResNetV2 for the Hindi Image Captioning task. Our submission tops in English->Hindi Multimodal task in its track and Odia<->English translation tasks. Also, our submissions performed well in the Indic Multilingual tasks.