Search Results for author: S apat

Found 13 papers, 1 papers with code

Code-mixed parse trees and how to find them

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.

INMT: Interactive Neural Machine Translation Prediction

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.

Machine Translation Translation

Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data

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

Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification

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.

Classification Domain Adaptation +5

A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading

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.

Sequential Pattern Mining

MTWatch: A Tool for the Analysis of Noisy Parallel Data

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.

Classification General Classification +3

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