Learning to Ask: Neural Question Generation for Reading Comprehension
Xinya Du,Junru Shao,Claire Cardie +2 more
- Vol. 1, pp 1342-1352
TLDR
This paper proposed an attention-based sequence learning model for question generation from text passages in reading comprehension, which is trainable end-to-end via sequence-tosequence learning and significantly outperforms the state-of-the-art rule-based system.Abstract:
We study automatic question generation for sentences from text passages in reading comprehension. We introduce an attention-based sequence learning model for the task and investigate the effect of encoding sentence- vs. paragraph-level information. In contrast to all previous work, our model does not rely on hand-crafted rules or a sophisticated NLP pipeline; it is instead trainable end-to-end via sequence-to-sequence learning. Automatic evaluation results show that our system significantly outperforms the state-of-the-art rule-based system. In human evaluations, questions generated by our system are also rated as being more natural (i.e.,, grammaticality, fluency) and as more difficult to answer (in terms of syntactic and lexical divergence from the original text and reasoning needed to answer).read more
Citations
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Journal ArticleDOI
Joint Learning of Question Answering and Question Generation
Yibo Sun,Duyu Tang,Nan Duan,Tao Qin,Shujie Liu,Zhao Yan,Ming Zhou,Yuanhua Lv,Wenpeng Yin,Xiaocheng Feng,Bing Qin,Ting Liu +11 more
TL;DR: Two training algorithms for learning better QA and QG models through leveraging one another are presented and it is found that the performance of a QG model could be easily improved by aQA model via policy gradient, however, directly applying GAN that regards all the generated questions as negative instances could not improve the accuracy of the QA model.
Book ChapterDOI
Automating Reading Comprehension by Generating Question and Answer Pairs
Vishwajeet Kumar,Vishwajeet Kumar,Kireeti Boorla,Yogesh Kumar Meena,Ganesh Ramakrishnan,Yuan-Fang Li +5 more
TL;DR: This article presented a two-stage process to generate question-answer pairs from the text, where the first stage encodes the span of the pivotal answer in the sentence using Pointer Networks and the second stage employs sequence to sequence models for question generation, enhanced with rich linguistic features.
Posted Content
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
Patrick S. H. Lewis,Yuxiang Wu,Linqing Liu,Pasquale Minervini,Heinrich Küttler,Aleksandra Piktus,Pontus Stenetorp,Sebastian Riedel,Sebastian Riedel +8 more
TL;DR: The authors proposed RePAQAQ, a new QA-pair retriever that preempts and caches test questions to match the accuracy of recent retrieve-and-read models, whilst being significantly faster.
Posted Content
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering
TL;DR: Open-domain Question Answering (OpenQA) is an important task in NLP, which aims to answer a question in the form of natural language based on large-scale unstructured documents as mentioned in this paper.
Proceedings ArticleDOI
Let’s Ask Again: Refine Network for Automatic Question Generation
Preksha Nema,Akash Kumar Mohankumar,Mitesh M. Khapra,Balaji Vasan Srinivasan,Balaraman Ravindran +4 more
TL;DR: RefNet as discussed by the authors uses a dual attention network which pays attention to both the original passage and the question (initial draft) generated by the first decoder, thereby making it more correct and complete.
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