no code implementations • NAACL (CALCS) 2021 • Ramakrishna Appicharla, Kamal Kumar Gupta, Asif Ekbal, Pushpak Bhattacharyya
We submit a neural machine translation (NMT) system which is trained on the synthetic code-mixed (cm) English-Hinglish parallel corpus.
1 code implementation • AMTA 2022 • Baban Gain, Ramakrishna Appicharla, Asif Ekbal, Muthusamy Chelliah, Soumya Chennabasavraj, Nikesh Garera
Chatbots are used in various sectors such as banking, healthcare, e-commerce, etc, and are mainly available in English.
no code implementations • ICON 2021 • Ramakrishna Appicharla, Asif Ekbal, Pushpak Bhattacharyya
In this paper, we explore various approaches to build Hindi to Bengali Neural Machine Translation (NMT) systems for the educational domain.
no code implementations • ACL (WAT) 2021 • Ramakrishna Appicharla, Kamal Kumar Gupta, Asif Ekbal, Pushpak Bhattacharyya
This paper describes the systems submitted to WAT 2021 MultiIndicMT shared task by IITP-MT team.
1 code implementation • 23 Oct 2023 • Baban Gain, Ramakrishna Appicharla, Soumya Chennabasavaraj, Nikesh Garera, Asif Ekbal, Muthusamy Chelliah
Translating questions using Neural Machine Translation (NMT) poses more challenges, especially in noisy environments, where the grammatical correctness of the questions is not monitored.
no code implementations • 11 Aug 2023 • Ramakrishna Appicharla, Baban Gain, Santanu Pal, Asif Ekbal
In this paper, we further explore this idea by evaluating with context-aware pronoun translation test set by training multi-encoder models trained on three different context settings viz, previous two sentences, random two sentences, and a mix of both as context.