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        <title>Entity Tracking Improves Cloze-style Reading Comprehension</title>
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    <name type="personal">
        <namePart type="given">Luong</namePart>
        <namePart type="family">Hoang</namePart>
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        <namePart type="given">Sam</namePart>
        <namePart type="family">Wiseman</namePart>
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    <name type="personal">
        <namePart type="given">Alexander</namePart>
        <namePart type="family">Rush</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Recent work has improved on modeling for reading comprehension tasks with simple approaches such as the Attention Sum-Reader; however, automatic systems still significantly trail human performance. Analysis suggests that many of the remaining hard instances are related to the inability to track entity-references throughout documents. This work focuses on these hard entity tracking cases with two extensions: (1) additional entity features, and (2) training with a multi-task tracking objective. We show that these simple modifications improve performance both independently and in combination, and we outperform the previous state of the art on the LAMBADA dataset by 8 pts, particularly on difficult entity examples. We also effectively match the performance of more complicated models on the named entity portion of the CBT dataset.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1130</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Entity Tracking Improves Cloze-style Reading Comprehension
%A Hoang, Luong
%A Wiseman, Sam
%A Rush, Alexander
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F hoang-etal-2018-entity
%X Recent work has improved on modeling for reading comprehension tasks with simple approaches such as the Attention Sum-Reader; however, automatic systems still significantly trail human performance. Analysis suggests that many of the remaining hard instances are related to the inability to track entity-references throughout documents. This work focuses on these hard entity tracking cases with two extensions: (1) additional entity features, and (2) training with a multi-task tracking objective. We show that these simple modifications improve performance both independently and in combination, and we outperform the previous state of the art on the LAMBADA dataset by 8 pts, particularly on difficult entity examples. We also effectively match the performance of more complicated models on the named entity portion of the CBT dataset.
%R 10.18653/v1/D18-1130
%U https://aclanthology.org/D18-1130
%U https://doi.org/10.18653/v1/D18-1130
%P 1049-1055

Markdown (Informal)

[Entity Tracking Improves Cloze-style Reading Comprehension](https://aclanthology.org/D18-1130) (Hoang et al., EMNLP 2018)

• Entity Tracking Improves Cloze-style Reading Comprehension (Hoang et al., EMNLP 2018)

ACL

• Luong Hoang, Sam Wiseman, and Alexander Rush. 2018. Entity Tracking Improves Cloze-style Reading Comprehension. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1049–1055, Brussels, Belgium. Association for Computational Linguistics.