Search Results for author: Pushpak Bhattacharya

Found 5 papers, 0 papers with code

Neural Machine Translation in Low-Resource Setting: a Case Study in English-Marathi Pair

no code implementations MTSummit 2021 Aakash Banerjee, Aditya Jain, Shivam Mhaskar, Sourabh Dattatray Deoghare, Aman Sehgal, Pushpak Bhattacharya

Techniques such as Phrase Table Injection (PTI) and back-translation and mixing of language corpora are used for enhancing the parallel data; whereas pivoting and multilingual embeddings are used to leverage transfer learning.

Machine Translation NMT +2

Leveraging Alignment and Phonology for low-resource Indic to English Neural Machine Transliteration

no code implementations ICON 2020 Parth Patel, Manthan Mehta, Pushpak Bhattacharya, Arjun Atreya

In this paper we present a novel transliteration technique based on Orthographic Syllable(OS) segmentation for low-resource Indian languages (ILs).

Transliteration

Technology Pipeline for Large Scale Cross-Lingual Dubbing of Lecture Videos into Multiple Indian Languages

no code implementations1 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.

Chunking Speech Synthesis +1

End-to-End Relation Extraction using Markov Logic Networks

no code implementations4 Dec 2017 Sachin Pawar, Pushpak Bhattacharya, Girish K. Palshikar

Our end-to-end relation extraction performance is better than 2 out of 3 previous results reported on the ACE 2004 dataset.

Relation Relation Extraction +1

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