no code implementations • CL (ACL) 2021 • Payal Khullar
Abstract This article describes an experiment to evaluate the impact of different types of ellipses discussed in theoretical linguistics on Neural Machine Translation (NMT), using English to Hindi/Telugu as source and target languages.
no code implementations • EACL 2021 • Payal Khullar
The present paper investigates the impact of the anaphoric one words in English on the Neural Machine Translation (NMT) process using English-Hindi as source and target language pair.
no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Payal Khullar
Computational approaches to noun ellipsis resolution has been sparse, with only a naive rule-based approach that uses syntactic feature constraints for marking noun ellipsis licensors and selecting their antecedents.
no code implementations • CONLL 2020 • Payal Khullar, Arghya Bhattacharya, Manish Shrivastava
One-anaphora has figured prominently in theoretical linguistic literature, but computational linguistics research on the phenomenon is sparse.
no code implementations • LREC 2020 • Payal Khullar, Kushal Majmundar, Manish Shrivastava
Ellipsis resolution has been identified as an important step to improve the accuracy of mainstream Natural Language Processing (NLP) tasks such as information retrieval, event extraction, dialog systems, etc.
no code implementations • RANLP 2019 • Payal Khullar, Allen Antony, Manish Shrivastava
We get an F1-score of 76. 47{\%} for detection and 70. 27{\%} for NPE resolution on the testset.
no code implementations • ACL 2018 • Payal Khullar, Konigari Rachna, Mukul Hase, Manish Shrivastava
This paper presents a system that automatically generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences.