@inproceedings{bhambhoria-etal-2020-smart,
title = "A Smart System to Generate and Validate Question Answer Pairs for {COVID}-19 Literature",
author = "Bhambhoria, Rohan and
Feng, Luna and
Sepehr, Dawn and
Chen, John and
Cowling, Conner and
Kocak, Sedef and
Dolatabadi, Elham",
editor = "Chandrasekaran, Muthu Kumar and
de Waard, Anita and
Feigenblat, Guy and
Freitag, Dayne and
Ghosal, Tirthankar and
Hovy, Eduard and
Knoth, Petr and
Konopnicki, David and
Mayr, Philipp and
Patton, Robert M. and
Shmueli-Scheuer, Michal",
booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sdp-1.4",
doi = "10.18653/v1/2020.sdp-1.4",
pages = "20--30",
abstract = "Automatically generating question answer (QA) pairs from the rapidly growing coronavirus-related literature is of great value to the medical community. Creating high quality QA pairs would allow researchers to build models to address scientific queries for answers which are not readily available in support of the ongoing fight against the pandemic. QA pair generation is, however, a very tedious and time consuming task requiring domain expertise for annotation and evaluation. In this paper we present our contribution in addressing some of the challenges of building a QA system without gold data. We first present a method to create QA pairs from a large semi-structured dataset through the use of transformer and rule-based models. Next, we propose a means of engaging subject matter experts (SMEs) for annotating the QA pairs through the usage of a web application. Finally, we demonstrate some experiments showcasing the effectiveness of leveraging active learning in designing a high performing model with a substantially lower annotation effort from the domain experts.",
}
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%0 Conference Proceedings
%T A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature
%A Bhambhoria, Rohan
%A Feng, Luna
%A Sepehr, Dawn
%A Chen, John
%A Cowling, Conner
%A Kocak, Sedef
%A Dolatabadi, Elham
%Y Chandrasekaran, Muthu Kumar
%Y de Waard, Anita
%Y Feigenblat, Guy
%Y Freitag, Dayne
%Y Ghosal, Tirthankar
%Y Hovy, Eduard
%Y Knoth, Petr
%Y Konopnicki, David
%Y Mayr, Philipp
%Y Patton, Robert M.
%Y Shmueli-Scheuer, Michal
%S Proceedings of the First Workshop on Scholarly Document Processing
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F bhambhoria-etal-2020-smart
%X Automatically generating question answer (QA) pairs from the rapidly growing coronavirus-related literature is of great value to the medical community. Creating high quality QA pairs would allow researchers to build models to address scientific queries for answers which are not readily available in support of the ongoing fight against the pandemic. QA pair generation is, however, a very tedious and time consuming task requiring domain expertise for annotation and evaluation. In this paper we present our contribution in addressing some of the challenges of building a QA system without gold data. We first present a method to create QA pairs from a large semi-structured dataset through the use of transformer and rule-based models. Next, we propose a means of engaging subject matter experts (SMEs) for annotating the QA pairs through the usage of a web application. Finally, we demonstrate some experiments showcasing the effectiveness of leveraging active learning in designing a high performing model with a substantially lower annotation effort from the domain experts.
%R 10.18653/v1/2020.sdp-1.4
%U https://aclanthology.org/2020.sdp-1.4
%U https://doi.org/10.18653/v1/2020.sdp-1.4
%P 20-30
Markdown (Informal)
[A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature](https://aclanthology.org/2020.sdp-1.4) (Bhambhoria et al., sdp 2020)
ACL