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        <title>SCDE: Sentence Cloze Dataset with High Quality Distractors From Examinations</title>
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        <namePart type="given">Xiang</namePart>
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    <name type="personal">
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            <namePart type="given">Dan</namePart>
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    <abstract>We introduce SCDE, a dataset to evaluate the performance of computational models through sentence prediction. SCDE is a human created sentence cloze dataset, collected from public school English examinations. Our task requires a model to fill up multiple blanks in a passage from a shared candidate set with distractors designed by English teachers. Experimental results demonstrate that this task requires the use of non-local, discourse-level context beyond the immediate sentence neighborhood. The blanks require joint solving and significantly impair each other’s context. Furthermore, through ablations, we show that the distractors are of high quality and make the task more challenging. Our experiments show that there is a significant performance gap between advanced models (72%) and humans (87%), encouraging future models to bridge this gap.</abstract>
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%0 Conference Proceedings
%T SCDE: Sentence Cloze Dataset with High Quality Distractors From Examinations
%A Kong, Xiang
%A Gangal, Varun
%A Hovy, Eduard
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F kong-etal-2020-scde
%X We introduce SCDE, a dataset to evaluate the performance of computational models through sentence prediction. SCDE is a human created sentence cloze dataset, collected from public school English examinations. Our task requires a model to fill up multiple blanks in a passage from a shared candidate set with distractors designed by English teachers. Experimental results demonstrate that this task requires the use of non-local, discourse-level context beyond the immediate sentence neighborhood. The blanks require joint solving and significantly impair each other’s context. Furthermore, through ablations, we show that the distractors are of high quality and make the task more challenging. Our experiments show that there is a significant performance gap between advanced models (72%) and humans (87%), encouraging future models to bridge this gap.
%R 10.18653/v1/2020.acl-main.502
%U https://aclanthology.org/2020.acl-main.502
%U https://doi.org/10.18653/v1/2020.acl-main.502
%P 5668-5683

Markdown (Informal)

[SCDE: Sentence Cloze Dataset with High Quality Distractors From Examinations](https://aclanthology.org/2020.acl-main.502) (Kong et al., ACL 2020)

• SCDE: Sentence Cloze Dataset with High Quality Distractors From Examinations (Kong et al., ACL 2020)

ACL

• Xiang Kong, Varun Gangal, and Eduard Hovy. 2020. SCDE: Sentence Cloze Dataset with High Quality Distractors From Examinations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5668–5683, Online. Association for Computational Linguistics.