no code implementations • RANLP 2021 • Hema Ala, Vandan Mujadia, Dipti Sharma
In this paper, we present a novel approachfor domain adaptation in Neural MachineTranslation which aims to improve thetranslation quality over a new domain. Adapting new domains is a highly challeng-ing task for Neural Machine Translation onlimited data, it becomes even more diffi-cult for technical domains such as Chem-istry and Artificial Intelligence due to spe-cific terminology, etc.
no code implementations • LREC 2022 • Vandan Mujadia, Dipti Sharma
We present the Hindi-Telugu Parallel Corpus of different technical domains such as Natural Science, Computer Science, Law and Healthcare along with the General domain.
no code implementations • MTSummit 2021 • Vandan Mujadia, Dipti Misra Sharma
In this paper, we (team - oneNLP-IIITH) describe our Neural Machine Translation approaches for English-Marathi (both direction) for LoResMT-20211 .
no code implementations • WMT (EMNLP) 2020 • Vandan Mujadia, Dipti Sharma
This paper describes the participation of team F1toF6 (LTRC, IIIT-Hyderabad) for the WMT 2020 task, similar language translation.
no code implementations • WMT (EMNLP) 2021 • Vandan Mujadia, Dipti Sharma
This paper describes the participation of team oneNLP (LTRC, IIIT-Hyderabad) for the WMT 2021 task, similar language translation.
no code implementations • 3 Apr 2024 • Vandan Mujadia, Pruthwik Mishra, Arafat Ahsan, Dipti Misra Sharma
We constructed a translation evaluation task where we performed zero-shot learning, in-context example-driven learning, and fine-tuning of large language models to provide a score out of 100, where 100 represents a perfect translation and 1 represents a poor translation.
no code implementations • 22 Dec 2023 • Nikhilesh Bhatnagar, Ashok Urlana, Vandan Mujadia, Pruthwik Mishra, Dipti Misra Sharma
We analyze the data and propose methods to match articles to video descriptions that serve as document and summary pairs.
no code implementations • 15 Nov 2023 • Vandan Mujadia, Ashok Urlana, Yash Bhaskar, Penumalla Aditya Pavani, Kukkapalli Shravya, Parameswari Krishnamurthy, Dipti Misra Sharma
In this work, our aim is to explore the multilingual capabilities of large language models by using machine translation as a task involving English and 22 Indian languages.
no code implementations • 1 Nov 2022 • Anusha Prakash, Arun Kumar, Ashish Seth, Bhagyashree Mukherjee, Ishika Gupta, Jom Kuriakose, Jordan Fernandes, K V Vikram, Mano Ranjith Kumar M, Metilda Sagaya Mary, Mohammad Wajahat, Mohana N, Mudit Batra, Navina K, Nihal John George, Nithya Ravi, Pruthwik Mishra, Sudhanshu Srivastava, Vasista Sai Lodagala, Vandan Mujadia, Kada Sai Venkata Vineeth, Vrunda Sukhadia, Dipti Sharma, Hema Murthy, Pushpak Bhattacharya, S Umesh, Rajeev Sangal
Cross-lingual dubbing of lecture videos requires the transcription of the original audio, correction and removal of disfluencies, domain term discovery, text-to-text translation into the target language, chunking of text using target language rhythm, text-to-speech synthesis followed by isochronous lipsyncing to the original video.
no code implementations • ICON 2021 • Arafat Ahsan, Vandan Mujadia, Dipti Misra Sharma
We present findings from a first in-depth post-editing effort estimation study in the English-Hindi direction along multiple effort indicators.