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        <title>Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems</title>
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        <namePart type="given">Ankur</namePart>
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            <namePart type="given">Eneko</namePart>
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            <namePart type="given">Marianna</namePart>
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        <name type="personal">
            <namePart type="given">Ivan</namePart>
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    <abstract>Information extraction systems analyze text to produce entities and beliefs, but their output often has errors. In this paper, we analyze the reading consistency of the extracted facts with respect to the text from which they were derived and show how to detect and correct errors. We consider both the scenario when the provenance text is automatically found by an information extraction system and when it is curated by humans. We contrast consistency with credibility; define and explore consistency and repair tasks; and demonstrate a simple yet effective and generalizable model. We analyze these tasks and evaluate this approach on three datasets. Against a strong baseline model, we consistently improve both consistency and repair across three datasets using a simple MLP model with attention and lexical features.</abstract>
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    <identifier type="doi">10.18653/v1/2022.deelio-1.5</identifier>
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%0 Conference Proceedings
%T Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems
%A Padia, Ankur
%A Ferraro, Francis
%A Finin, Tim
%Y Agirre, Eneko
%Y Apidianaki, Marianna
%Y Vulić, Ivan
%S Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland and Online
%F padia-etal-2022-jointly
%X Information extraction systems analyze text to produce entities and beliefs, but their output often has errors. In this paper, we analyze the reading consistency of the extracted facts with respect to the text from which they were derived and show how to detect and correct errors. We consider both the scenario when the provenance text is automatically found by an information extraction system and when it is curated by humans. We contrast consistency with credibility; define and explore consistency and repair tasks; and demonstrate a simple yet effective and generalizable model. We analyze these tasks and evaluate this approach on three datasets. Against a strong baseline model, we consistently improve both consistency and repair across three datasets using a simple MLP model with attention and lexical features.
%R 10.18653/v1/2022.deelio-1.5
%U https://aclanthology.org/2022.deelio-1.5/
%U https://doi.org/10.18653/v1/2022.deelio-1.5
%P 42-52

Markdown (Informal)

[Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems](https://aclanthology.org/2022.deelio-1.5/) (Padia et al., DeeLIO 2022)

• Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems (Padia et al., DeeLIO 2022)

ACL

• Ankur Padia, Francis Ferraro, and Tim Finin. 2022. Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems. In Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 42–52, Dublin, Ireland and Online. Association for Computational Linguistics.