@article{kocisky-etal-2018-narrativeqa,
title = "The {N}arrative{QA} Reading Comprehension Challenge",
author = "Ko{\v{c}}isk{\'y}, Tom{\'a}{\v{s}} and
Schwarz, Jonathan and
Blunsom, Phil and
Dyer, Chris and
Hermann, Karl Moritz and
Melis, G{\'a}bor and
Grefenstette, Edward",
editor = "Lee, Lillian and
Johnson, Mark and
Toutanova, Kristina and
Roark, Brian",
journal = "Transactions of the Association for Computational Linguistics",
volume = "6",
year = "2018",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q18-1023",
doi = "10.1162/tacl_a_00023",
pages = "317--328",
abstract = "Reading comprehension (RC){---}in contrast to information retrieval{---}requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.",
}
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<abstract>Reading comprehension (RC)—in contrast to information retrieval—requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.</abstract>
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%0 Journal Article
%T The NarrativeQA Reading Comprehension Challenge
%A Kočiský, Tomáš
%A Schwarz, Jonathan
%A Blunsom, Phil
%A Dyer, Chris
%A Hermann, Karl Moritz
%A Melis, Gábor
%A Grefenstette, Edward
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F kocisky-etal-2018-narrativeqa
%X Reading comprehension (RC)—in contrast to information retrieval—requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.
%R 10.1162/tacl_a_00023
%U https://aclanthology.org/Q18-1023
%U https://doi.org/10.1162/tacl_a_00023
%P 317-328
Markdown (Informal)
[The NarrativeQA Reading Comprehension Challenge](https://aclanthology.org/Q18-1023) (Kočiský et al., TACL 2018)
ACL
- Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018. The NarrativeQA Reading Comprehension Challenge. Transactions of the Association for Computational Linguistics, 6:317–328.