@inproceedings{saikh-etal-2020-scholarlyread,
title = "{S}cholarly{R}ead: A New Dataset for Scientific Article Reading Comprehension",
author = "Saikh, Tanik and
Ekbal, Asif and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.675",
pages = "5498--5504",
abstract = "We present ScholarlyRead, span-of-word-based scholarly articles{'} Reading Comprehension (RC) dataset with approximately 10K manually checked passage-question-answer instances. ScholarlyRead was constructed in semi-automatic way. We consider the articles from two popular journals of a reputed publishing house. Firstly, we generate questions from these articles in an automatic way. Generated questions are then manually checked by the human annotators. We propose a baseline model based on Bi-Directional Attention Flow (BiDAF) network that yields the F1 score of 37.31{\%}. The framework would be useful for building Question-Answering (QA) systems on scientific articles.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>We present ScholarlyRead, span-of-word-based scholarly articles’ Reading Comprehension (RC) dataset with approximately 10K manually checked passage-question-answer instances. ScholarlyRead was constructed in semi-automatic way. We consider the articles from two popular journals of a reputed publishing house. Firstly, we generate questions from these articles in an automatic way. Generated questions are then manually checked by the human annotators. We propose a baseline model based on Bi-Directional Attention Flow (BiDAF) network that yields the F1 score of 37.31%. The framework would be useful for building Question-Answering (QA) systems on scientific articles.</abstract>
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%0 Conference Proceedings
%T ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension
%A Saikh, Tanik
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F saikh-etal-2020-scholarlyread
%X We present ScholarlyRead, span-of-word-based scholarly articles’ Reading Comprehension (RC) dataset with approximately 10K manually checked passage-question-answer instances. ScholarlyRead was constructed in semi-automatic way. We consider the articles from two popular journals of a reputed publishing house. Firstly, we generate questions from these articles in an automatic way. Generated questions are then manually checked by the human annotators. We propose a baseline model based on Bi-Directional Attention Flow (BiDAF) network that yields the F1 score of 37.31%. The framework would be useful for building Question-Answering (QA) systems on scientific articles.
%U https://aclanthology.org/2020.lrec-1.675
%P 5498-5504
Markdown (Informal)
[ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension](https://aclanthology.org/2020.lrec-1.675) (Saikh et al., LREC 2020)
ACL