no code implementations • ACL 2020 • Simran Khanuja, D, S apat, ipan, Anirudh Srinivasan, Sunayana Sitaram, Monojit Choudhury
We present results on all these tasks using cross-lingual word embedding models and multilingual models.
no code implementations • LREC 2020 • Anirudh Srinivasan, D, S apat, ipan, Monojit Choudhury
In this paper, we explore the methods of obtaining parse trees of code-mixed sentences and analyse the obtained trees.
1 code implementation • IJCNLP 2019 • Sebastin Santy, D, S apat, ipan, Monojit Choudhury, Kalika Bali
In this paper, we demonstrate an Interactive Machine Translation interface, that assists human translators with on-the-fly hints and suggestions.
no code implementations • ACL 2018 • Adithya Pratapa, Gayatri Bhat, Monojit Choudhury, Sunayana Sitaram, D, S apat, ipan, Kalika Bali
Training language models for Code-mixed (CM) language is known to be a difficult problem because of lack of data compounded by the increased confusability due to the presence of more than one language.
Automatic Speech Recognition (ASR) Language Identification +3
no code implementations • ACL 2018 • Raksha Sharma, Pushpak Bhattacharyya, D, S apat, ipan, Himanshu Sharad Bhatt
In this paper, we propose that words that do not change their polarity and significance represent the transferable (usable) information across domains for cross-domain sentiment classification.
no code implementations • WS 2016 • Shourya Roy, D, S apat, ipan, Y. Narahari
We offer a fluctuation smoothing computational approach for unsupervised automatic short answer grading (ASAG) techniques in the educational ecosystem.
no code implementations • LREC 2014 • D, S apat, ipan, Declan Groves
State-of-the-art statistical machine translation (SMT) technique requires a good quality parallel data to build a translation model.