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        <title>From Dataset Recycling to Multi-Property Extraction and Beyond</title>
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        <namePart type="given">Tomasz</namePart>
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    <name type="personal">
        <namePart type="given">Michał</namePart>
        <namePart type="family">Pietruszka</namePart>
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    <name type="personal">
        <namePart type="given">Łukasz</namePart>
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        <namePart type="given">Jakub</namePart>
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    <name type="personal">
        <namePart type="given">Filip</namePart>
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            <title>Proceedings of the 24th Conference on Computational Natural Language Learning</title>
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    <abstract>This paper investigates various Transformer architectures on the WikiReading Information Extraction and Machine Reading Comprehension dataset. The proposed dual-source model outperforms the current state-of-the-art by a large margin. Next, we introduce WikiReading Recycled - a newly developed public dataset, and the task of multiple-property extraction. It uses the same data as WikiReading but does not inherit its predecessor’s identified disadvantages. In addition, we provide a human-annotated test set with diagnostic subsets for a detailed analysis of model performance.</abstract>
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    <identifier type="doi">10.18653/v1/2020.conll-1.52</identifier>
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%0 Conference Proceedings
%T From Dataset Recycling to Multi-Property Extraction and Beyond
%A Dwojak, Tomasz
%A Pietruszka, Michał
%A Borchmann, Łukasz
%A Chłędowski, Jakub
%A Graliński, Filip
%S Proceedings of the 24th Conference on Computational Natural Language Learning
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F dwojak-etal-2020-dataset
%X This paper investigates various Transformer architectures on the WikiReading Information Extraction and Machine Reading Comprehension dataset. The proposed dual-source model outperforms the current state-of-the-art by a large margin. Next, we introduce WikiReading Recycled - a newly developed public dataset, and the task of multiple-property extraction. It uses the same data as WikiReading but does not inherit its predecessor’s identified disadvantages. In addition, we provide a human-annotated test set with diagnostic subsets for a detailed analysis of model performance.
%R 10.18653/v1/2020.conll-1.52
%U https://aclanthology.org/2020.conll-1.52
%U https://doi.org/10.18653/v1/2020.conll-1.52
%P 641-651

Markdown (Informal)

[From Dataset Recycling to Multi-Property Extraction and Beyond](https://aclanthology.org/2020.conll-1.52) (Dwojak et al., CoNLL 2020)

• From Dataset Recycling to Multi-Property Extraction and Beyond (Dwojak et al., CoNLL 2020)

ACL

• Tomasz Dwojak, Michał Pietruszka, Łukasz Borchmann, Jakub Chłędowski, and Filip Graliński. 2020. From Dataset Recycling to Multi-Property Extraction and Beyond. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 641–651, Online. Association for Computational Linguistics.