The NarrativeQA Reading Comprehension Challenge
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.
PDF Abstract TACL 2018 PDF TACL 2018 AbstractCode
Datasets
Introduced in the Paper:
NarrativeQA
Used in the Paper:
SQuAD
MS MARCO
NewsQA
SearchQA
MCTest
CBT
Children's Book Test
BookTest
Results from the Paper
Ranked #9 on
Question Answering
on NarrativeQA
(BLEU-1 metric)
