Multiple Perspective Answer Reranking for Multi-passage Reading Comprehension

Ren, Mucheng; Huang, Heyan; Wei, Ran; Liu, Hongyu; Bai, Yu; Wang, Yang

doi:10.1007/978-3-030-32236-6_67

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…. .. BLEU (Weissenborn et al 2017), , (Tan et al 2018a), , (Wang et al 2018e), (Bauer et al 2018), , (Indurthi et al 2018), (Tay et al 2018), (Ke et al 2018), (Yan et al 2019), (Wang et al 2019e), (Jin et al 2019, (Mihaylov and Frank 2019), (Bi et al 2019), (Ren et al 2019), (Nishida et al 2019b), (Liu et al 2019b), (Tay et al 2019), , (Wang et al 2020a), (Nakatsuji and Okui 2020), (Zhou et al 2020b) (Bauer et al 2018), (Indurthi et al 2018), (Tay et al 2018), (Tay et al 2019), . .…”

Section: Table A5 Continuedmentioning

confidence: 99%

A Survey on Machine Reading Comprehension Systems

2022

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Machine Reading Comprehension (MRC) is a challenging task and hot topic in Natural Language Processing. The goal of this field is to develop systems for answering the questions regarding a given context. In this paper, we present a comprehensive survey on diverse aspects of MRC systems, including their approaches, structures, input/outputs, and research novelties. We illustrate the recent trends in this field based on a review of 241 papers published during 2016–2020. Our investigation demonstrated that the focus of research has changed in recent years from answer extraction to answer generation, from single- to multi-document reading comprehension, and from learning from scratch to using pre-trained word vectors. Moreover, we discuss the popular datasets and the evaluation metrics in this field. The paper ends with an investigation of the most-cited papers and their contributions.

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