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    <abstract>Recent question answering and machine reading benchmarks frequently reduce the task to one of pinpointing spans within a certain text passage that answers the given question. Typically, these systems are not required to actually understand the text on a deeper level that allows for more complex reasoning on the information contained. We introduce a new dataset called BiQuAD that requires deeper comprehension in order to answer questions in both extractive and deductive fashion. The dataset consist of 4,190 closed-domain texts and a total of 99,149 question-answer pairs. The texts are synthetically generated soccer match reports that verbalize the main events of each match. All texts are accompanied by a structured Datalog program that represents a (logical) model of its information. We show that state-of-the-art QA models do not perform well on the challenging long form contexts and reasoning requirements posed by the dataset. In particular, transformer based state-of-the-art models achieve F1-scores of only 39.0. We demonstrate how these synthetic datasets align structured knowledge with natural text and aid model introspection when approaching complex text understanding.</abstract>
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%0 Conference Proceedings
%T BiQuAD: Towards QA based on deeper text understanding
%A Grimm, Frank
%A Cimiano, Philipp
%S Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics
%D 2021
%8 aug
%I Association for Computational Linguistics
%C Online
%F grimm-cimiano-2021-biquad
%X Recent question answering and machine reading benchmarks frequently reduce the task to one of pinpointing spans within a certain text passage that answers the given question. Typically, these systems are not required to actually understand the text on a deeper level that allows for more complex reasoning on the information contained. We introduce a new dataset called BiQuAD that requires deeper comprehension in order to answer questions in both extractive and deductive fashion. The dataset consist of 4,190 closed-domain texts and a total of 99,149 question-answer pairs. The texts are synthetically generated soccer match reports that verbalize the main events of each match. All texts are accompanied by a structured Datalog program that represents a (logical) model of its information. We show that state-of-the-art QA models do not perform well on the challenging long form contexts and reasoning requirements posed by the dataset. In particular, transformer based state-of-the-art models achieve F1-scores of only 39.0. We demonstrate how these synthetic datasets align structured knowledge with natural text and aid model introspection when approaching complex text understanding.
%R 10.18653/v1/2021.starsem-1.10
%U https://aclanthology.org/2021.starsem-1.10
%U https://doi.org/10.18653/v1/2021.starsem-1.10
%P 105-115

Markdown (Informal)

[BiQuAD: Towards QA based on deeper text understanding](https://aclanthology.org/2021.starsem-1.10) (Grimm & Cimiano, *SEM 2021)

• BiQuAD: Towards QA based on deeper text understanding (Grimm & Cimiano, *SEM 2021)

ACL

• Frank Grimm and Philipp Cimiano. 2021. BiQuAD: Towards QA based on deeper text understanding. In Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics, pages 105–115, Online. Association for Computational Linguistics.