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        <title>No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension</title>
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        <namePart type="given">Xuguang</namePart>
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        <namePart type="given">Linjun</namePart>
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    <name type="personal">
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    <abstract>The Natural Questions (NQ) benchmark set brings new challenges to Machine Reading Comprehension: the answers are not only at different levels of granularity (long and short), but also of richer types (including no-answer, yes/no, single-span and multi-span). In this paper, we target at this challenge and handle all answer types systematically. In particular, we propose a novel approach called Reflection Net which leverages a two-step training procedure to identify the no-answer and wrong-answer cases. Extensive experiments are conducted to verify the effectiveness of our approach. At the time of paper writing (May. 20, 2020), our approach achieved the top 1 on both long and short answer leaderboard, with F1 scores of 77.2 and 64.1, respectively.</abstract>
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%0 Conference Proceedings
%T No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension
%A Wang, Xuguang
%A Shou, Linjun
%A Gong, Ming
%A Duan, Nan
%A Jiang, Daxin
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-answer-better
%X The Natural Questions (NQ) benchmark set brings new challenges to Machine Reading Comprehension: the answers are not only at different levels of granularity (long and short), but also of richer types (including no-answer, yes/no, single-span and multi-span). In this paper, we target at this challenge and handle all answer types systematically. In particular, we propose a novel approach called Reflection Net which leverages a two-step training procedure to identify the no-answer and wrong-answer cases. Extensive experiments are conducted to verify the effectiveness of our approach. At the time of paper writing (May. 20, 2020), our approach achieved the top 1 on both long and short answer leaderboard, with F1 scores of 77.2 and 64.1, respectively.
%R 10.18653/v1/2020.findings-emnlp.370
%U https://aclanthology.org/2020.findings-emnlp.370
%U https://doi.org/10.18653/v1/2020.findings-emnlp.370
%P 4141-4150

Markdown (Informal)

[No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension](https://aclanthology.org/2020.findings-emnlp.370) (Wang et al., Findings 2020)

• No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension (Wang et al., Findings 2020)

ACL

• Xuguang Wang, Linjun Shou, Ming Gong, Nan Duan, and Daxin Jiang. 2020. No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4141–4150, Online. Association for Computational Linguistics.