Papers by Payal Khullar

4 papers
Automatic Question Generation using Relative Pronouns and Adverbs (P18-3)

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Challenge: Automatic Question Generation is a system that generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences.
Approach: They propose a system that automatically generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences.
Outcome: The proposed system generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences.
Exploring Statistical and Neural Models for Noun Ellipsis Detection and Resolution in English (2020.aacl-srw)

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Challenge: Existing approaches to noun ellipsis resolution have been sparse, using syntactic feature constraints for marking licensors and selecting their antecedents.
Approach: They propose to use supervised machine learning to improve the existing F1 score by 16.55% and resolution by 14.97% for noun ellipsis subtasks.
Outcome: The proposed framework improves the existing F1 score by 16.55% and the resolution subtask by 14.97%.
Why Find the Right One? (2021.eacl-srw)

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Challenge: a new study investigates the impact of anaphoric one words in English on the neural machine translation process.
Approach: They investigate the impact of anaphoric one words in English on the Neural Machine Translation process using English-Hindi as source and target language pair.
Outcome: The proposed system performs poorly on sentences containing anaphoric ones compared to sentences involving regular, non-anaphorical ones . the results show that amongst the anamorphic words, the noun class is clearly much harder for NMT than the determinatives .
NoEl: An Annotated Corpus for Noun Ellipsis in English (2020.lrec-1)

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Challenge: Ellipsis resolution is an important step to improve the accuracy of mainstream natural language processing tasks such as information retrieval, event extraction, dialog systems, etc.
Approach: They extend the study of ellipsis by annotating a corpus for noun ellippsis and closely related phenomenon using the first hundred movies of Cornell Movie Dialogs Dataset.
Outcome: The proposed corpus has 946 instances of exophoric and endophorical noun ellipsis, making it the biggest resource of nouns in English, to the best of our knowledge.

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