Deep Questions without Deep Understanding

Labutov, Igor; Basu, Sumit; Vanderwende, Lucy

doi:10.3115/v1/p15-1086

Cited by 123 publications

(95 citation statements)

References 11 publications

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“…Early works on Question Generation were essentially rule based systems (Heilman and Smith, 2010;Mostow and Chen, 2009;Lindberg et al, 2013;Labutov et al, 2015). Current models for AQG are based on the encode-attend-decode paradigm and they either generate questions from the passage alone Yao et al, 2018) or from the passage and Passage: McLetchie was elected on the Lothian regional list and the Conservatives suffered a net loss of five seats , with leader Annabel Goldie claiming that their support had held firm, nevertheless, she too announced she would step down as leader of the party.…”

Section: Related Workmentioning

confidence: 99%

Let’s Ask Again: Refine Network for Automatic Question Generation

Nema¹,

Mohankumar²,

Khapra³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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In this work, we focus on the task of Automatic Question Generation (AQG) where given a passage and an answer the task is to generate the corresponding question. It is desired that the generated question should be (i) grammatically correct (ii) answerable from the passage and (iii) specific to the given answer. An analysis of existing AQG models shows that they produce questions which do not adhere to one or more of the abovementioned qualities. In particular, the generated questions look like an incomplete draft of the desired question with a clear scope for refinement. To alleviate this shortcoming, we propose a method which tries to mimic the human process of generating questions by first creating an initial draft and then refining it. More specifically, we propose Refine Network (RefNet) which contains two decoders. The second decoder uses a dual attention network which pays attention to both (i) the original passage and (ii) the question (initial draft) generated by the first decoder. In effect, it refines the question generated by the first decoder, thereby making it more correct and complete. We evaluate RefNet on three datasets, viz., SQuAD, HOTPOT-QA, and DROP, and show that it outperforms existing state-of-the-art methods by 7-16% on all of these datasets. Lastly, we show that we can improve the quality of the second decoder on specific metrics, such as, fluency and answerability by explicitly rewarding revisions that improve on the corresponding metric during training. The code has been made publicly available 1 .

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Section: Related Workmentioning

confidence: 99%

Let’s Ask Again: Refine Network for Automatic Question Generation

Nema¹,

Mohankumar²,

Khapra³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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show abstract

“…Labutov et al (2015) proposed an 'ontologycrowd-relevance' method for question generation. First, Freebase types and Wikipedia session names are used as semantic tags to understand texts.…”

Section: Question Generation For Qamentioning

confidence: 99%

Question Generation for Question Answering

Duan¹,

Tang²,

Chen³

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

247

132

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This paper presents how to generate questions from given passages using neural networks, where large scale QA pairs are automatically crawled and processed from Community-QA website, and used as training data. The contribution of the paper is 2-fold: First, two types of question generation approaches are proposed, one is a retrieval-based method using convolution neural network (CNN), the other is a generation-based method using recurrent neural network (RNN); Second, we show how to leverage the generated questions to improve existing question answering systems. We evaluate our question generation method for the answer sentence selection task on three benchmark datasets, including SQuAD, MS MARCO, and WikiQA. Experimental results show that, by using generated questions as an extra signal, significant QA improvement can be achieved.

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“…Clarification questions are not. Outside reading comprehension questions, Labutov et al (2015) studied the problem of generating question templates via crowdsourcing, Liu et al (2010) use template-based question generation to help authors write better related work sections, Mostafazadeh et al (2016) consider question generation from images, and Artzi and Zettlemoyer (2011) use human-generated clarification questions to drive a semantic parser.…”

Section: Related Workmentioning

confidence: 99%

Are You Asking the Right Questions? Teaching Machines to Ask Clarification Questions

Rao¹

2017

Proceedings of ACL 2017, Student Research Workshop

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Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this thesis work, we explore how can we teach machines to ask clarification questions when faced with uncertainty, a goal of increasing importance in today's automated society. We do a preliminary study using data from StackExchange, a plentiful online resource where people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We build neural network models inspired by the idea of the expected value of perfect information: a good question is one whose expected answer is going to be most useful. To build generalizable systems, we propose two future research directions: a template-based model and a sequence-to-sequence based neural generative model.

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Deep Questions without Deep Understanding

Cited by 123 publications

References 11 publications

Let’s Ask Again: Refine Network for Automatic Question Generation

Let’s Ask Again: Refine Network for Automatic Question Generation

Question Generation for Question Answering

Are You Asking the Right Questions? Teaching Machines to Ask Clarification Questions

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