no code implementations • WS 2019 • Loitongbam Sanayai Meetei, Thoudam Doren Singh, B, Sivaji yopadhyay
In this paper, we carried out an extensive comparison to evaluate the benefits of using a multimodal approach on translating text in English to a low resource language, Hindi as a part of WAT2019 shared task.
no code implementations • WS 2019 • Sahinur Rahman Laskar, Rohit Pratap Singh, Partha Pakray, B, Sivaji yopadhyay
With the widespread use of Machine Trans-lation (MT) techniques, attempt to minimizecommunication gap among people from di-verse linguistic backgrounds.
no code implementations • WS 2019 • Sahinur Rahman Laskar, Partha Pakray, B, Sivaji yopadhyay
Also, we have achieved BLEU score 53. 7 (Hindi to Nepali) and 49. 1 (Nepali to Hindi) in contrastive system type.
no code implementations • WS 2017 • Sainik Mahata, Dipankar Das, B, Sivaji yopadhyay
A Statistical Machine Translation (SMT) system is always trained using large parallel corpus to produce effective translation.
no code implementations • COLING 2016 • Braja Gopal Patra, Dipankar Das, B, Sivaji yopadhyay
Finally, we developed mood classification systems using Support Vector Machines and Feed Forward Neural Networks based on the features collected from audio, lyrics, and a combination of both.
no code implementations • LREC 2014 • Santanu Pal, Sudip Kumar Naskar, B, Sivaji yopadhyay
Reordering poses a big challenge in statistical machine translation between distant language pairs.