@inproceedings{boratko-etal-2018-systematic,
title = "A Systematic Classification of Knowledge, Reasoning, and Context within the {ARC} Dataset",
author = "Boratko, Michael and
Padigela, Harshit and
Mikkilineni, Divyendra and
Yuvraj, Pritish and
Das, Rajarshi and
McCallum, Andrew and
Chang, Maria and
Fokoue-Nkoutche, Achille and
Kapanipathi, Pavan and
Mattei, Nicholas and
Musa, Ryan and
Talamadupula, Kartik and
Witbrock, Michael",
editor = "Choi, Eunsol and
Seo, Minjoon and
Chen, Danqi and
Jia, Robin and
Berant, Jonathan",
booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-2607",
doi = "10.18653/v1/W18-2607",
pages = "60--70",
abstract = "The recent work of Clark et al. (2018) introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into easy and challenge sets. That paper includes an analysis of 100 questions with respect to the types of knowledge and reasoning required to answer them; however, it does not include clear definitions of these types, nor does it offer information about the quality of the labels. We propose a comprehensive set of definitions of knowledge and reasoning types necessary for answering the questions in the ARC dataset. Using ten annotators and a sophisticated annotation interface, we analyze the distribution of labels across the challenge set and statistics related to them. Additionally, we demonstrate that although naive information retrieval methods return sentences that are irrelevant to answering the query, sufficient supporting text is often present in the (ARC) corpus. Evaluating with human-selected relevant sentences improves the performance of a neural machine comprehension model by 42 points.",
}
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<abstract>The recent work of Clark et al. (2018) introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into easy and challenge sets. That paper includes an analysis of 100 questions with respect to the types of knowledge and reasoning required to answer them; however, it does not include clear definitions of these types, nor does it offer information about the quality of the labels. We propose a comprehensive set of definitions of knowledge and reasoning types necessary for answering the questions in the ARC dataset. Using ten annotators and a sophisticated annotation interface, we analyze the distribution of labels across the challenge set and statistics related to them. Additionally, we demonstrate that although naive information retrieval methods return sentences that are irrelevant to answering the query, sufficient supporting text is often present in the (ARC) corpus. Evaluating with human-selected relevant sentences improves the performance of a neural machine comprehension model by 42 points.</abstract>
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%0 Conference Proceedings
%T A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset
%A Boratko, Michael
%A Padigela, Harshit
%A Mikkilineni, Divyendra
%A Yuvraj, Pritish
%A Das, Rajarshi
%A McCallum, Andrew
%A Chang, Maria
%A Fokoue-Nkoutche, Achille
%A Kapanipathi, Pavan
%A Mattei, Nicholas
%A Musa, Ryan
%A Talamadupula, Kartik
%A Witbrock, Michael
%Y Choi, Eunsol
%Y Seo, Minjoon
%Y Chen, Danqi
%Y Jia, Robin
%Y Berant, Jonathan
%S Proceedings of the Workshop on Machine Reading for Question Answering
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F boratko-etal-2018-systematic
%X The recent work of Clark et al. (2018) introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into easy and challenge sets. That paper includes an analysis of 100 questions with respect to the types of knowledge and reasoning required to answer them; however, it does not include clear definitions of these types, nor does it offer information about the quality of the labels. We propose a comprehensive set of definitions of knowledge and reasoning types necessary for answering the questions in the ARC dataset. Using ten annotators and a sophisticated annotation interface, we analyze the distribution of labels across the challenge set and statistics related to them. Additionally, we demonstrate that although naive information retrieval methods return sentences that are irrelevant to answering the query, sufficient supporting text is often present in the (ARC) corpus. Evaluating with human-selected relevant sentences improves the performance of a neural machine comprehension model by 42 points.
%R 10.18653/v1/W18-2607
%U https://aclanthology.org/W18-2607
%U https://doi.org/10.18653/v1/W18-2607
%P 60-70
Markdown (Informal)
[A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset](https://aclanthology.org/W18-2607) (Boratko et al., ACL 2018)
ACL
- Michael Boratko, Harshit Padigela, Divyendra Mikkilineni, Pritish Yuvraj, Rajarshi Das, Andrew McCallum, Maria Chang, Achille Fokoue-Nkoutche, Pavan Kapanipathi, Nicholas Mattei, Ryan Musa, Kartik Talamadupula, and Michael Witbrock. 2018. A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset. In Proceedings of the Workshop on Machine Reading for Question Answering, pages 60–70, Melbourne, Australia. Association for Computational Linguistics.