Other Works

This section contains references to related research work and publications by Emil Biju.

Vocabulary-constrained Question Generation with Rare Word Masking and Dual Attention

Abstract

Question generation is the task of generating questions from a text passage that can be answered using information available in the passage. Known models for question generation are trained to predict words from a large, predefined vocabulary. However, a large vocabulary increases memory usage, training and inference times and a predefined vocabulary may not include context-specific words from the input passage. In this paper, we propose a neural question generation framework that generates semantically accurate and context-specific questions using a small-size vocabulary. We break the question generation task into two subtasks namely, generating the skeletal structure of a question using common words from the vocabulary and pointing to rare words from the input passage to complete the question.

Publication

This work has been published as a conference paper at the ACM India Joint International Conference on Data Science and Management of Data (CODS-COMAD 2021). The paper was awarded the Best Paper Honorable Mention by the conference committee. To learn more about this work, please click on the links below:

Publication PDF Slides Video

Citation

If you are referring to this work, please cite it as follows:

@inproceedings{10.1145/3430984.3431074,
author = {Biju, Emil},
title = {Vocabulary-Constrained Question Generation with Rare Word Masking and Dual Attention},
year = {2021},
isbn = {9781450388177},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3430984.3431074},
doi = {10.1145/3430984.3431074},
booktitle = {8th ACM IKDD CODS and 26th COMAD},
pages = {431},
numpages = {1},
location = {Bangalore, India},
series = {CODS COMAD 2021}
}