Neural Question Generation

Notes:

Neural question generation is a type of natural language processing task that involves using artificial intelligence algorithms to automatically generate questions from a given piece of text. This involves training a machine learning model on a large collection of question-answer pairs, where the model is given a piece of text and is asked to generate a question that is related to the text.

One potential application of neural question generation is in education, where it could be used to generate practice questions for students. For example, a teacher could provide a model with a passage of text, and the model could generate a series of questions that are related to the text. This could help students to better understand the material and to improve their critical thinking skills.

Another potential application of neural question generation is in customer service, where it could be used to generate frequently asked questions (FAQs) for a website or product. For example, a company could provide a neural question generation model with a description of their product, and the model could generate a list of questions that are commonly asked by customers. This could help the company to provide better support to their customers and to improve the overall customer experience.

Overall, neural question generation is a type of natural language processing task that involves using machine learning algorithms to automatically generate questions from a given piece of text. It has potential applications in education and customer service, among other areas.

  • Neural network
  • Neural networks
  • Neural question generator
  • Neural questions
  • NQG (Neural Question Generation)
  • Question generation system

References:

See also:

Automatic Question Generation 2019 | Question Generation Meta Guide

Improving neural question generation using answer separation
Y Kim, H Lee, J Shin, K Jung – Proceedings of the AAAI Conference on …, 2019 – aaai.org
Neural question generation (NQG) is the task of generating a question from a given passage with deep neural networks. Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the …

Recent advances in neural question generation
L Pan, W Lei, TS Chua, MY Kan – arXiv preprint arXiv:1905.08949, 2019 – arxiv.org
Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition. These trends point to NQG as a bellwether for NLP, about how human intelligence embodies the skills of …

Self-attention architectures for answer-agnostic neural question generation
T Scialom, B Piwowarski, J Staiano – … of the 57th Annual Meeting of the …, 2019 – aclweb.org
Neural architectures based on self-attention, such as Transformers, recently attracted interest from the research community, and obtained significant improvements over the state of the art in several tasks. We explore how Transformers can be adapted to the task of …

Improving Neural Question Generation using World Knowledge
D Gupta, K Suleman, M Adada, A McNamara… – arXiv preprint arXiv …, 2019 – arxiv.org
In this paper, we propose a method for incorporating world knowledge (linked entities and fine-grained entity types) into a neural question generation model. This world knowledge helps to encode additional information related to the entities present in the passage required …

Evaluating rewards for question generation models
T Hosking, S Riedel – arXiv preprint arXiv:1902.11049, 2019 – arxiv.org
… Xinya Du and Claire Cardie. 2018. Harvest- ing Paragraph-Level Question-Answer Pairs from Wikipedia. Xinya Du, Junru Shao, and Claire Cardie. 2017. Learn- ing to Ask: Neural Question Generation for Reading Comprehension. pages 1342–1352. Alex Graves. 2012 …

Reinforcement learning based graph-to-sequence model for natural question generation
Y Chen, L Wu, MJ Zaki – arXiv preprint arXiv:1908.04942, 2019 – arxiv.org
Page 1. Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation Yu Chen? Rensselaer Polytechnic Institute cheny39@rpi.edu Lingfei Wu? IBM Research lwu@email.wm.edu Mohammed J. Zaki Rensselaer Polytechnic Institute zaki@cs.rpi …

Improving question generation with to the point context
J Li, Y Gao, L Bing, I King, MR Lyu – arXiv preprint arXiv:1910.06036, 2019 – arxiv.org
Page 1. Improving Question Generation With to the Point Context Jingjing Li1? Yifan Gao1? Lidong Bing2 Irwin King1 Michael R. Lyu1 1 Department of Computer Science and Engineering, The Chinese University of Hong Kong …

Let’s Ask Again: Refine Network for Automatic Question Generation
P Nema, AK Mohankumar, MM Khapra… – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Let’s Ask Again: Refine Network for Automatic Question Generation Preksha Nema†‡? Akash Kumar Mohankumar†? Mitesh M. Khapra†‡ Balaji Vasan Srinivasan• Balaraman Ravindran†‡ †IIT Madras, India •Adobe …

Natural question generation with reinforcement learning based graph-to-sequence model
Y Chen, L Wu, MJ Zaki – arXiv preprint arXiv:1910.08832, 2019 – arxiv.org
… [8] X. Du, J. Shao, and C. Cardie. Learning to ask: Neural question generation for reading comprehension. arXiv preprint arXiv:1705.00106, 2017 … [12] Y. Kim, H. Lee, J. Shin, and K. Jung. Improving neural question generation using answer separation …

Reinforced dynamic reasoning for conversational question generation
B Pan, H Li, Z Yao, D Cai, H Sun – arXiv preprint arXiv:1907.12667, 2019 – arxiv.org
Page 1. Reinforced Dynamic Reasoning for Conversational Question Generation Boyuan Pan1?, Hao Li1, Ziyu Yao2, Deng Cai1,3, Huan Sun2 1State Key Lab of CAD&CG, Zhejiang University 2The Ohio State University 3Alibaba …

Question-type Driven Question Generation
W Zhou, M Zhang, Y Wu – arXiv preprint arXiv:1909.00140, 2019 – arxiv.org
… 2018. Answer-focused and position-aware neural question generation. In Pro- ceedings of the 2018 Conference on Empirical Meth- ods in Natural Language Processing, Brussels, Bel- gium, October 31 – November 4, 2018, pages 3930– 3939 …

A comparative study on question-worthy sentence selection strategies for educational question generation
G Chen, J Yang, D Gasevic – International Conference on Artificial …, 2019 – Springer
… In contrast to rule-based methods, neural question generation methods can capture complex question generation patterns from data without handcrafted rules, thus being much more effective and scalable. Typically, neural question …

Cross-lingual training for automatic question generation
V Kumar, N Joshi, A Mukherjee… – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Cross-Lingual Training for Automatic Question Generation Vishwajeet Kumar1,2, Nitish Joshi2, Arijit Mukherjee2, Ganesh Ramakrishnan2, and Preethi Jyothi2 1IITB-Monash Research Academy, Mumbai, India 2IIT Bombay …

Multi-task learning with language modeling for question generation
W Zhou, M Zhang, Y Wu – arXiv preprint arXiv:1908.11813, 2019 – arxiv.org
… Xinya Du, Junru Shao, and Claire Cardie. 2017. Learn- ing to ask: Neural question generation for reading comprehension … Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yan- jun Ma, and Shi Wang. 2018. Answer-focused and position-aware neural question generation …

Difficulty-controllable multi-hop question generation from knowledge graphs
V Kumar, Y Hua, G Ramakrishnan, G Qi, L Gao… – International Semantic …, 2019 – Springer
… Knowl. Inf. Syst. 55(3), 529–569 (2018)CrossRefGoogle Scholar. 9. Du, X., Shao, J., Cardie, C.: Learning to ask: neural question generation for reading comprehension. In: ACL, vol. 1, pp. 1342–1352 (2017)Google Scholar. 10 …

Interconnected question generation with coreference alignment and conversation flow modeling
Y Gao, P Li, I King, MR Lyu – arXiv preprint arXiv:1906.06893, 2019 – arxiv.org
Page 1. Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling Yifan Gao1? Piji Li2 Irwin King1 Michael R. Lyu1 1 Department of Computer Science and Engineering, The Chinese …

A Recurrent BERT-based Model for Question Generation
YH Chan, YC Fan – Proceedings of the 2nd Workshop on Machine …, 2019 – aclweb.org
Page 1. Proceedings of the Second Workshop on Machine Reading for Question Answering, pages 154–162 Hong Kong, China, November 4, 2019. c 2019 Association for Computational Linguistics 154 A Recurrent BERT-based Model for Question Generation …

A multi-agent communication framework for question-worthy phrase extraction and question generation
S Wang, Z Wei, Z Fan, Y Liu, X Huang – … of the AAAI Conference on Artificial …, 2019 – aaai.org
… 2015), Du et al. (2017) propose to apply sequence-to- sequence model with attention mechanism for question gen- eration without human designed rules, which is called neural … question generation (NQG), and has become the state of the art …

Addressing semantic drift in question generation for semi-supervised question answering
S Zhang, M Bansal – arXiv preprint arXiv:1909.06356, 2019 – arxiv.org
Page 1. Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering Shiyue Zhang Mohit Bansal UNC Chapel Hill 1shiyue, mbansall@cs.unc.edu Abstract Text-based Question Generation (QG …

Answer-Focused and Position-Aware Neural Network for Transfer Learning in Question Generation
K Zi, X Sun, Y Cao, S Wang, X Feng, Z Ma… – … on Knowledge Science …, 2019 – Springer
… arXiv preprint arXiv:1611.09268 (2016). 9. Sun, XW, Liu, J., Lyu, Y., He, W., Ma, YJ, Wang, S.: Answer-focused and position-aware neural question generation … Yuan, X., et al.: Machine comprehension by text-to-text neural question generation …

Extended Answer and Uncertainty Aware Neural Question Generation
H Zeng, Z Zhi, J Liu, B Wei – arXiv preprint arXiv:1911.08112, 2019 – arxiv.org
In this paper, we study automatic question generation, the task of creating questions from corresponding text passages where some certain spans of the text can serve as the answers. We propose an Extended Answer-aware Network (EAN) which is trained with …

Learning to generate questions with adaptive copying neural networks
X Lu – Proceedings of the 2019 International Conference on …, 2019 – dl.acm.org
… The recent work in [2] proposed a neural question generation model based on LSTM which demonstrates good empirical results … 2017. Learning to Ask: Neural Question Generation for Reading Comprehension. CoRR abs/1705.00106 (2017) …

Joint learning of question answering and question generation
Y Sun, D Tang, N Duan, T Qin, S Liu… – … on Knowledge and …, 2019 – ieeexplore.ieee.org
Page 1. Joint Learning of Question Answering and Question Generation Yibo Sun , Duyu Tang, Nan Duan, Tao Qin, Member, IEEE, Shujie Liu, Zhao Yan , Ming Zhou, Yuanhua Lv, Wenpeng Yin, Xiaocheng Feng, Bing Qin, and Ting Liu …

Question Generation by Transformers
K Kriangchaivech, A Wangperawong – arXiv preprint arXiv:1909.05017, 2019 – arxiv.org
… [Du, Shao, and Cardie 2017] Du, X.; Shao, J.; and Cardie, C. 2017. Learning to ask: Neural question generation for read- ing comprehension … [Kim et al. 2019] Kim, Y.; Lee, H.; Shin, J.; and Jung, K. 2019. Improving neural question generation using answer separation …

Learning to generate questions by learningwhat not to generate
B Liu, M Zhao, D Niu, K Lai, Y He, H Wei… – The World Wide Web …, 2019 – dl.acm.org
… Existing neural question generation models are not sufficient mainly due to their inability to properly model the process of how each word in the question is selected, ie, whether repeat- ing the given passage or being generated from a vocabulary …

Neural Question Generation using Interrogative Phrases
Y Sasazawa, S Takase, N Okazaki – Proceedings of the 12th …, 2019 – aclweb.org
Question Generation (QG) is the task of generating questions from a given passage. One of the key requirements of QG is to generate a question such that it results in a target answer. Previous works used a target answer to obtain a desired question. However, we also want to …

Combination of Statistical and Neural Approaches in the Japanese Question Generation System
L Nio, K Murakami – The Association for Natural Language Processing, 2019 – anlp.jp
… 2) This way we can obtain the language model PLM (q) and separate the translation model P(c|q). 3.3 Neural Question Generation Here we generate the question with the neural ma- chine translation (NMT) technique. In contrast …

SAC-Net: Stroke-Aware Copy Network for Chinese Neural Question Generation
W Li, Q Kang, B Xu, L Zhang – 2019 IEEE International …, 2019 – ieeexplore.ieee.org
Question Generation aims to create various questions from a given passage, which can provide education material, improve the training of question answering, and help chat bots have cold-to-start or continue to talk to people. Chinese Question Generation is a new …

Putting the horse before the cart: A generator-evaluator framework for question generation from text
V Kumar, G Ramakrishnan, YF Li – Proceedings of the 23rd Conference …, 2019 – aclweb.org
Page 1. Proceedings of the 23rd Conference on Computational Natural Language Learning, pages 812–821 Hong Kong, China, November 3-4, 2019. c 2019 Association for Computational Linguistics 812 Putting the Horse …

Ask to Learn: A Study on Curiosity-driven Question Generation
T Scialom, J Staiano – arXiv preprint arXiv:1911.03350, 2019 – arxiv.org
Page 1. Ask to Learn: A Study on Curiosity-driven Question Generation Thomas Scialom*‡, Jacopo Staiano‡ * Sorbonne Université, CNRS, LIP6, F-75005 Paris, France ‡ reciTAL, Paris, France {thomas,jacopo}@recital.ai Abstract …

Modeling question asking using neural program generation
Z Wang, BM Lake – arXiv preprint arXiv:1907.09899, 2019 – arxiv.org
… [23] Xingdi Yuan, Tong Wang, Caglar Gulcehre, Alessandro Sordoni, Philip Bachman, Sandeep Subramanian, Saizheng Zhang, and Adam Trischler. Machine comprehension by text-to-text neural question generation. In Workshop on Representation Learning for NLP, 2017 …

Unified language model pre-training for natural language understanding and generation
L Dong, N Yang, W Wang, F Wei, X Liu… – Advances in Neural …, 2019 – papers.nips.cc
Paper accepted and presented at the Neural Information Processing Systems Conference (http://nips.cc/).

based Question Generation with Adaptive Instance Transfer and Augmentation
Q Yu, L Bing, Q Zhang, W Lam, L Si – arXiv preprint arXiv:1911.01556, 2019 – arxiv.org
Page 1. Review-based Question Generation with Adaptive Instance Transfer and Augmentation ? Qian Yu1, Lidong Bing2, Qiong Zhang2, Wai Lam1, Luo Si2 1 The Chinese University of Hong Kong 2 Alibaba DAMO Academy …

Weak supervision enhanced generative network for question generation
Y Wang, J Zheng, Q Liu, Z Zhao, J Xiao… – arXiv preprint arXiv …, 2019 – arxiv.org
… [Du and Cardie, 2017] Xinya Du and Claire Cardie. Identi- fying where to focus in reading comprehension for neural question generation … [Du et al., 2017] Xinya Du, Junru Shao, and Claire Cardie. Learning to ask: Neural question generation for reading comprehension …

Towards Answer-unaware Conversational Question Generation
M Nakanishi, T Kobayashi, Y Hayashi – … of the 2nd Workshop on Machine …, 2019 – aclweb.org
Page 1. Proceedings of the Second Workshop on Machine Reading for Question Answering, pages 63–71 Hong Kong, China, November 4, 2019. c 2019 Association for Computational Linguistics 63 Towards Answer-unaware Conversational Question Generation …

Question Generation from Paragraphs: A Tale of Two Hierarchical Models
V Kumar, R Chaki, ST Talluri, G Ramakrishnan… – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Question Generation from Paragraphs: A Tale of Two Hierarchical Models Vishwajeet Kumar1,2,3, Raktim Chaki2, Sai Teja Talluri2, Ganesh Ramakrishnan2, Yuan-Fang Li3, and Gholamreza Haffari3 1IITB-Monash …

Unsupervised Common Question Generation from Multiple Documents using Reinforced Contrastive Coordinator
WS Cho, Y Zhang, S Rao, A Celikyilmaz… – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Unsupervised Common Question Generation from Multiple Documents using Reinforced Contrastive Coordinator Woon Sang Cho? Yizhe Zhang† Sudha Rao† Asli Celikyilmaz† Chenyan Xiong† Jianfeng Gao† Mengdi …

BERT for Question Generation
YH Chan, YC Fan – Proceedings of the 12th International Conference on …, 2019 – aclweb.org
… arXiv preprint arXiv:1810.04805. Xinya Du, Junru Shao, and Claire Cardie. 2017. Learn- ing to ask: Neural question generation for reading comprehension. arXiv preprint arXiv:1705.00106. Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2002 …

Neural Question Generation
D Do, B Battogtokh – 2019 – cs229.stanford.edu
Question generation attempts to create natural questions from a body of text. An important application of this technology is related to education: we can use question generation to provide readily-available reading comprehension material given any corpus of text …

Easy-to-Hard: Leveraging Simple Questions for Complex Question Generation
J Zhao, X Deng, H Sun – arXiv preprint arXiv:1912.02367, 2019 – arxiv.org
Page 1. Easy-to-Hard: Leveraging Simple Questions for Complex Question Generation Jie Zhao, Xiang Deng, Huan Sun, The Ohio State University zhao.1359@osu.edu, deng.595@osu.edu, sun.397@osu.edu Abstract This …

Synthetic QA corpora generation with roundtrip consistency
C Alberti, D Andor, E Pitler, J Devlin… – arXiv preprint arXiv …, 2019 – arxiv.org
… Xinya Du, Junru Shao, and Claire Cardie. 2017. Learn- ing to ask: Neural question generation for reading comprehension. In Proceedings of the 55th An- nual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1342– 1352 …

Using Multiple Encoders for Chinese Neural Question Generation from the Knowledge Base
M Chen, J Zhao, M Liu – IOP Conference Series: Materials …, 2019 – iopscience.iop.org
Question generation is an important task in the field of natural language processing and intelligent tutoring system. Previous work on Chinese question generation focused on the rule-based approach, which requires a large amount of human resource to develop the …

Let Me Know What to Ask: Interrogative-Word-Aware Question Generation
J Kang, HPS Roman, SH Myaeng – arXiv preprint arXiv:1910.13794, 2019 – arxiv.org
Page 1. Let Me Know What to Ask: Interrogative-Word-Aware Question Generation Junmo Kang? Haritz Puerto San Roman? Sung-Hyon Myaeng School of Computing, KAIST Daejeon, Republic of Korea {junmo.kang, haritzpuerto94, myaeng}@kaist.ac.kr Abstract …

Distant Supervised Why-Question Generation with Passage Self-Matching Attention
J Hu, Z Li, R Wu, H Wang, A Liu, J Xu… – … Joint Conference on …, 2019 – ieeexplore.ieee.org
Page 1. Distant Supervised Why-Question Generation with Passage Self-Matching Attention Jiaxin Hu 1,2 , Zhixu Li 1,3? , Renshou Wu 1 , Hongling Wang 1 , An Liu 1 , Jiajie Xu 1 , Pengpeng Zhao 1 , Lei Zhao 1 1 School of …

Question Generation Based Product Information
K Xiao, X Zhou, Z Wang, X Duan, M Zhang – CCF International Conference …, 2019 – Springer
… question generation. In: INLG, pp. 51–60 (2016)Google Scholar. 10. Du, X., Shao, J., Cardie, C.: Learning to ask: neural question generation for reading comprehension. In: ACL, pp. 1342–1352 (2017)Google Scholar. 11. Zheng …

Watch and Ask: Video Question Generation
S Huang, S Hu, B Yan – International Conference on Neural Information …, 2019 – Springer
… evaluation for any target language. In: WMT@ACL, pp. 376–380 (2014)Google Scholar. 3. Du, X., Shao, J., Cardie, C.: Learning to ask: neural question generation for reading comprehension. In: ACL, vol. 1, pp.1342–1352 (2017 …

Keeping notes: Conditional natural language generation with a scratchpad encoder
R Benmalek, M Khabsa, S Desu, C Cardie… – Proceedings of the 57th …, 2019 – aclweb.org
Page 1. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4157–4167 Florence, Italy, July 28 – August 2, 2019. c 2019 Association for Computational Linguistics 4157 Keeping Notes …

Learning to ask unanswerable questions for machine reading comprehension
H Zhu, L Dong, F Wei, W Wang, B Qin, T Liu – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Learning to Ask Unanswerable Questions for Machine Reading Comprehension Haichao Zhu†?, Li Dong‡, Furu Wei‡, Wenhui Wang‡, Bing Qin†?, Ting Liu†? †Harbin Institute of Technology, Harbin, China ‡Microsoft …

Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation
W Wang, S Feng, D Wang, Y Zhang – Proceedings of the 2019 …, 2019 – aclweb.org
… Neural question generation (NQG) has been ex- tensively studied because of its broad application in question answering (QA) systems (Tang et al., 2018; Duan et al., 2017), reading comprehension (Kim et al., 2019; Sun et al., 2018), and visu- al question answering (Fan et al …

Triple-Joint Modeling for Question Generation Using Cross-Task Autoencoder
H Wang, R Wu, Z Li, Z Wang, Z Chen… – … Conference on Natural …, 2019 – Springer
… language. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 376–380 (2014)Google Scholar. 4. Du, X., Shao, J., Cardie, C.: Learning to ask: neural question generation for reading comprehension. In …

Convolutional Neural Network and Question Generation Based Approaches to Select Best Answers for Non-Factoid Questions
M Srinath – 2019 – etda.libraries.psu.edu
Page 1. The Pennsylvania State University The Graduate School College of Information Sciences and Technology CONVOLUTIONAL NEURAL NETWORK AND QUESTION GENERATION BASED APPROACHES TO SELECT BEST …

Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models
WS Cho, Y Zhang, S Rao, C Brockett, S Lee – arXiv preprint arXiv …, 2019 – arxiv.org
… 2017. Learn- ing to ask: Neural question generation for reading comprehension. In Proceedings of the 55th An- nual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1342– 1352, Vancouver, Canada …

Separate Answer Decoding for Multi-class Question Generation
K Wu, Y Hong, M Zhu, H Tang… – … Conference on Asian …, 2019 – ieeexplore.ieee.org
… 10, no. 2, pp. 194–204, 2017. [6] X. Du, J. Shao, and C. Cardie, “Learning to ask: Neural question generation for reading comprehension,” in Pro- ceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) …

Revisiting Paraphrase Question Generator using Pairwise Discriminator
BN Patro, D Chauhan, VK Kurmi… – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Revisiting Paraphrase Question Generator using Pairwise Discriminator Badri N. Patro1 , Dev Chauhan2, Vinod K. Kurmi1, Vinay P. Namboodiri2 1 Department of Electrical Engineering, Indian Institute of Technology …

Controlling the Specificity of Clarification Question Generation
YT Cao, S Rao, H Daumé III – Proceedings of the 2019 Workshop on …, 2019 – winlp.org
… Xinya Du, Junru Shao, and Claire Cardie. 2017. Learning to ask: Neural question generation for reading compre- hension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 1342–1352 …

Key Phrase Extraction for Generating Educational Question-Answer Pairs
A Willis, G Davis, S Ruan, L Manoharan… – Proceedings of the …, 2019 – dl.acm.org
… scalable applications of question generation. We describe and implement an end-to-end neural question generation system that generates question and answer pairs given a context paragraph only. We accomplish this by first …

Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds
T Klein, M Nabi – arXiv preprint arXiv:1911.02365, 2019 – arxiv.org
… answer focused question. Our result of neural question generation from text on the SQuAD 1.1 dataset (Rajpurkar et al., 2016) suggests that our method can produce seman- tically correct and diverse questions. Addi- tionally …

Generating Highly Relevant Questions
J Qiu, D Xiong – arXiv preprint arXiv:1910.03401, 2019 – arxiv.org
… Xinya Du, Junru Shao, and Claire Cardie. 2017. Learn- ing to ask: Neural question generation for reading comprehension. meeting of the association for com- putational linguistics, 1:1342–1352. Nan Duan, Duyu Tang, Peng Chen, and Ming Zhou. 2017 …

Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data
H Shahidi, M Li, J Lin – arXiv preprint arXiv:1909.10158, 2019 – arxiv.org
… 2.2 Neural Question Generation Previous NQG models can be classified into rule- based and neural-network-based approaches. Du et al … 2017. Learn- ing to ask: Neural question generation for reading comprehension. arXiv preprint arXiv:1705.00106 …

Detection of Similar Answers to Avoid Duplicate Question in Retrieval-based Automatic Question Generation
YS Choi, KJ Lee – KIPS Transactions on Software and Data …, 2019 – koreascience.or.kr
… Association for Computational Linguistics, Austin, Texas, pp. 2383-2392, 2016. X. Du, J. Shao, and C. Cardie, “Learning to Ask: Neural Question Generation for Reading Comprehension,” arXiv preprint arXiv:1705.00106, 2017 …

Automatic Question Generation based on MOOC Video Subtitles and Knowledge Graph
L Ma, Y Ma – Proceedings of the 2019 7th International Conference …, 2019 – dl.acm.org
… Neural Question Generation from Text: A Preliminary Study[J]. 2017 … In EACL (1). Association for Computational Linguistics, pages 376–385 [10] Du X , Shao J , Cardie C . Learning to Ask: Neural Question Generation for Reading Comprehension[J]. 2017 …

Anaphora Reasoning Question Generation Using Entity Coreference
K Hasegawa, T Matsumoto, T Mitamura – 2019 – anlp.jp
… We apply our system to Wikipedia articles and, based on our evaluation, our system generates more Anaphora Reasoning Question compared to the current state-of-the-art neural question generation model which intends to generate a paragraph-level question by around 30 …

Rule-Based Automatic Question Generation Using Semantic Role Labeling
O Keklik, T Tuglular, S Tekir – IEICE TRANSACTIONS on …, 2019 – search.ieice.org
… This is why Du et al. (2017) used BLEU, METEOR and ROUGE-L metrics to evaluate their neural question generation system. They also perform human evaluations to complement their results. BLEU metric is first proposed by IBM (Papineni et al, 2002) …

ASGen: Answer-containing Sentence Generation to Pre-Train Question Generator for Scale-up Data in Question Answering
A Kedia, SC Chinthakindi, S Back, H Lee, J Choo – 2019 – openreview.net
Page 1. Under review as a conference paper at ICLR 2020 ASGEN: ANSWER-CONTAINING SENTENCE GENERATION TO PRE-TRAIN QUESTION GENERATOR FOR DATA AUGMENTATION IN QUESTION ANSWERING Anonymous authors Paper under double-blind …

Neural Text Generation from Structured and Unstructured Data
H Shahidi – 2019 – uwspace.uwaterloo.ca
… data. Specifically, we consider neural table-to-text generation and neural question generation (NQG) tasks for text generation from structured and unstructured data respectively … model. 2.4 Neural Question Generation Models …

Generating question-answer hierarchies
K Krishna, M Iyyer – arXiv preprint arXiv:1906.02622, 2019 – arxiv.org
… We then condition a neural question generation system on these two classes, which enables us to generate both types of questions from a paragraph. We filter and structure these outputs using the techniques described in Section 3 …

Mass: Masked sequence to sequence pre-training for language generation
K Song, X Tan, T Qin, J Lu, TY Liu – arXiv preprint arXiv:1905.02450, 2019 – arxiv.org
Page 1. MASS: Masked Sequence to Sequence Pre-training for Language Generation Kaitao Song * 1 Xu Tan * 2 Tao Qin 2 Jianfeng Lu 1 Tie-Yan Liu 2 Abstract Pre-training and fine-tuning, eg, BERT (De- vlin et al., 2018), have …

Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss
C Liu, K Liu, S He, Z Nie, J Zhao – arXiv preprint arXiv:1910.13108, 2019 – arxiv.org
Page 1. Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss Cao Liu1,2, Kang Liu1,2, Shizhu He1,2, Zaiqing Nie3, Jun Zhao1,2 1 National Laboratory of Pattern Recognition …

Question Generation for Reading Comprehension of Language Learning Test:-A Method using Seq2Seq Approach with Transformer Model
J Shan, Y Nishihara, R Yamanishi… – … on Technologies and …, 2019 – ieeexplore.ieee.org
… 2016. [7] Du X, Shao J, Cardie C. “Learning to ask: Neural question generation for reading comprehension.” arXiv preprint arXiv:1705.00106. 2017. [8] Wang T, Yuan X, Trischler A. “A joint model for question answering and question generation.” arXiv preprint arXiv:1706.01450 …

ParaQG: A System for Generating Questions and Answers from Paragraphs
V Kumar, S Muneeswaran, G Ramakrishnan… – arXiv preprint arXiv …, 2019 – arxiv.org
… arXiv preprint arXiv:1810.04805. Xinya Du, Junru Shao, and Claire Cardie. 2017. Learn- ing to ask: Neural question generation for reading comprehension. In Proceedings of the 55th ACL, pages 1342–1352. ACL. Jiatao Gu, Zhengdong Lu, Hang Li, and Victor OK Li. 2016 …

Keeping Notes: Conditional Natural Language Generation with a Scratchpad Mechanism
RY Benmalek, M Khabsa, S Desu, C Cardie… – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Keeping Notes: Conditional Natural Language Generation with a Scratchpad Mechanism Ryan Y. Benmalek†?, Madian Khabsa‡?, Suma Desu¶?, Claire Cardie†, Michele Banko§? †Cornell University, ‡Facebook …

Question Answering for Fact-Checking
M Jobanputra – Proceedings of the Second Workshop on Fact …, 2019 – aclweb.org
… In literature, there are majorly two types of Question Generation systems: Rule- based and Neural Question Generation (NQG) model based. Ali et al … 2017. Learn- ing to ask: Neural question generation for reading comprehension. arXiv preprint arXiv:1705.00106 …

Neural Duplicate Question Detection without Labeled Training Data
A Rücklé, NS Moosavi, I Gurevych – arXiv preprint arXiv:1911.05594, 2019 – arxiv.org
Page 1. Neural Duplicate Question Detection without Labeled Training Data Andreas R ücklé and Nafise Sadat Moosavi and Iryna Gurevych Ubiquitous Knowledge Processing Lab (UKP) Department of Computer Science, Technische …

Unsupervised Question Answering for Fact-Checking
M Jobanputra – arXiv preprint arXiv:1910.07154, 2019 – arxiv.org
… In literature, there are majorly two types of Question Generation systems: Rule- based and Neural Question Generation (NQG) model based. Ali et al … 2017. Learn- ing to ask: Neural question generation for reading comprehension. arXiv preprint arXiv:1705.00106 …

Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums
Z Chai, X Xing, X Wan, B Huang – … of the 57th Annual Meeting of the …, 2019 – aclweb.org
Page 1. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5032–5046 Florence, Italy, July 28 – August 2, 2019. c 2019 Association for Computational Linguistics 5032 Asking the …

Question Generalization in Conversation
J Peng, S Zhong, P Li – … Symposium on Artificial Intelligence and Robotics, 2019 – Springer
… arXiv:1711.07614. 13. Du X, Shao J, Cardie C (2017) Learning to ask: neural question generation for reading comprehension. arXiv:1705.00106. 14. Nan D, Duyu T, Peng C, Ming Z (2017) Question generation for question answering. In: EMNLP …

Answer-based adversarial training for generating clarification questions
S Rao, H Daumé III – arXiv preprint arXiv:1904.02281, 2019 – arxiv.org
Page 1. Answer-based Adversarial Training for Generating Clarification Questions Sudha Rao? Microsoft Research, Redmond Sudha.Rao@microsoft.com Hal Daumé III University of Maryland, College Park Microsoft Research, New York City me@hal3.name Abstract …

Summarizing News Articles Using Question-and-Answer Pairs via Learning
X Wang, C Yu – International Semantic Web Conference, 2019 – Springer
… In: ICLR (2015)Google Scholar. 3. Chen, D., Fisch, A., Weston, J., Bordes, A.: Read wikipedia to answer open-domain questions. In: ACL (2017)Google Scholar. 4. Du, X., Cardie, C.: Identifying where to focus in reading comprehension for neural question generation …

Sparse Factor Analysis for Information Extraction and Fusion
RG Baraniuk – 2019 – apps.dtic.mil
… Over the past year, we have made significant progress on education data processing in five directions: 1) Statistical models for instructor content preference analysis, 2) Data-driven question generation models, 3) Criteria for Neural Question Generation Models, 4) Meta …

QuGAN: Quasi Generative Adversarial Network for Tibetan Question Answering Corpus Generation
Y Sun, C Chen, T Xia, X Zhao – IEEE Access, 2019 – ieeexplore.ieee.org
Page 1. Received July 3, 2019, accepted July 24, 2019, date of publication August 12, 2019, date of current version September 3, 2019. Digital Object Identifier 10.1109/ACCESS. 2019.2934581 QuGAN: Quasi Generative Adversarial Network for Tibetan Question Answering …

CALOR-QUEST: generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations
FBC Aloui, D Charlet, G Damnati, J Heinecke… – EMNLP 2019 MRQA …, 2019 – aclweb.org
… Adam Trischler. 2017. Ma- chine comprehension by text-to-text neural question generation. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 15–25. As- sociation for Computational Linguistics. 26

Populating the knowledge base of a conversational agent: human vs. machine
H Rodrigues, L Coheur, E Nyberg – Proceedings of the 34th ACM …, 2019 – dl.acm.org
… [5] X. Du, J. Shao, and C. Cardie. 2017. Learning to Ask: Neural Question Generation for Reading Comprehension. CoRR (2017). [6] Pedro Fialho, Luísa Coheur, Sérgio Curto, Pedro Cláudio, Ângela Costa, Alberto Abad, Hugo Meinedo, and Isabel Trancoso. 2013 …

CALOR-QUEST: generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations
F Béchet, C Aloui, D Charlet, G Damnati, J Heinecke… – 2019 – hal.archives-ouvertes.fr
Page 1. HAL Id: hal-02317018 https://hal.archives-ouvertes.fr/hal-02317018 Submitted on 15 Oct 2019 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not …

3Q: A 3-Layer Semantic Analysis Model for Question Suite Reduction
W Dai, S Sheni, T Hei – … Conference on Intelligent Science and Big Data …, 2019 – Springer
… Proc. VLDB Endow. 10(5), 565–576 (2017)CrossRefGoogle Scholar. 4. Du, X., Shao, J., Cardie, C.: Learning to ask: neural question generation for reading comprehension. arXiv preprint arXiv:1705.00106 (2017). 5. Hacioglu, K.: Semantic role labeling using dependency trees …

Unsupervised question answering by cloze translation
P Lewis, L Denoyer, S Riedel – arXiv preprint arXiv:1906.04980, 2019 – arxiv.org
Page 1. Unsupervised Question Answering by Cloze Translation Patrick Lewis Facebook AI Research University College London plewis@fb.com Ludovic Denoyer Facebook AI Research denoyer@fb.com Sebastian Riedel …

Proceedings of the 12th International Conference on Natural Language Generation
K van Deemter, C Lin, H Takamura – Proceedings of the 12th …, 2019 – aclweb.org
… 101 Neural Question Generation using Interrogative Phrases Yuichi Sasazawa, Sho Takase and Naoaki Okazaki … Neural Question Generation using Interrogative Phrases Yuichi Sasazawa, Sho Takase and Naoaki Okazaki …

Adversarial domain adaptation for machine reading comprehension
H Wang, Z Gan, X Liu, J Liu, J Gao, H Wang – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Adversarial Domain Adaptation for Machine Reading Comprehension Huazheng Wang1?, Zhe Gan2, Xiaodong Liu3, Jingjing Liu2, Jianfeng Gao3, Hongning Wang1 1University of Virginia, 2Microsoft Dynamics 365 AI Research, 3Microsoft Research …

Recurrent neural network-based semantic variational autoencoder for sequence-to-sequence learning
M Jang, S Seo, P Kang – Information Sciences, 2019 – Elsevier
JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …

Deep reinforcement learning for sequence-to-sequence models
Y Keneshloo, T Shi, N Ramakrishnan… – IEEE Transactions on …, 2019 – ieeexplore.ieee.org
Page 1. This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Deep Reinforcement Learning for …

LocalGAN: Modeling Local Distributions for Adversarial Response Generation
Z Xu, B Wang, H Zhang, K Qiu, D Zhang, C Sun – 2019 – openreview.net
Page 1. Under review as a conference paper at ICLR 2020 LOCALGAN: MODELING LOCAL DISTRIBUTIONS FOR ADVERSARIAL RESPONSE GENERATION Anonymous authors Paper under double-blind review ABSTRACT …

Towards Generating Math Word Problems from Equations and Topics
Q Zhou, D Huang – Proceedings of the 12th International Conference on …, 2019 – aclweb.org
Page 1. Proceedings of The 12th International Conference on Natural Language Generation, pages 494–503, Tokyo, Japan, 28 Oct – 1 Nov, 2019. c 2019 Association for Computational Linguistics 494 Towards Generating Math Word Problems from Equations and Topics …

Content Based Hierarchical URL Classification with Convolutional Neural Networks
K Maladkar – 2019 International Conference on Information …, 2019 – ieeexplore.ieee.org
… [8] Baghaee, Tina. “Automatic Neural Question Generation using Community-based Question Answering Systems.” (2017). [9] “A neural attention model for abstractive sentence summarization “. Alexander M Rush, Sumit Chopra, Jason Weston …

Discriminate and Reconstruct: Learning from Language Model to Answer Keyword Questions
B Pan, Y Yang, Y Zhuang, D Cai – 2019 2nd China Symposium …, 2019 – ieeexplore.ieee.org
… Reading for Question Answering, 2018, pp. 78–88. [23] X. Sun, J. Liu, Y. Lyu, W. He, Y. Ma, and S. Wang, “Answer-focused and position-aware neural question generation,” in Proceedings of the 2018 Conference on Empirical Me

Assessing the ability of self-attention networks to learn word order
B Yang, L Wang, DF Wong, LS Chao, Z Tu – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Assessing the Ability of Self-Attention Networks to Learn Word Order Baosong Yang† Longyue Wang‡ Derek F. Wong† Lidia S. Chao† Zhaopeng Tu‡? †NLP2CT Lab, Department of Computer and Information Science …

A Quality-Diversity Controllable GAN for Text Generation
X Lou, K Xu, Z Li, T Xia, S Wang, J Xiao – 2019 – openreview.net
… In Proceedings NAACL-HLT, 2019. Xinya Du, Junru Shao, and Claire Cardie. Learning to ask: Neural question generation for reading comprehension. In Proceedings of ACL, 2017. William Fedus, Ian J. Goodfellow, and Andrew M. Dai …

Proactive Knowledge-Goals Dialogue System Based on Pointer Network
H Zhou, C Chen, H Liu, F Qin, H Liang – CCF International Conference on …, 2019 – Springer
… in dialog systems. arXiv preprint arXiv:1902.04911 (2019). 21. Zhao, Y., Ni, X.: Paragraph-level neural question generation with maxout pointer and gated self-attention networks. In EMNLP (2018)Google Scholar. 22. Tam, Y.-C …

A Deep Learning Application for Automated Feature Extraction in Transaction-based Machine Learning
DC Woo, HS Moon, S Kwon, Y Cho – Journal of Information …, 2019 – koreascience.or.kr
Page 1. Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development …

What Do You MeanWhy?’: Resolving Sluices in Conversations
VPB Hansen, A Søgaard – arXiv preprint arXiv:1911.09478, 2019 – arxiv.org
Page 1. What Do You Mean ‘Why?’: Resolving Sluices in Conversations Victor Petrén Bach Hansen,1 2 Anders Søgaard1 3 1Department of Computer Science, University of Copenhagen, Denmark 2Topdanmark A/S, Denmark …

A Deep Generative Approach to Search Extrapolation and Recommendation
FX Han, D Niu, H Chen, K Lai, Y He, Y Xu – Proceedings of the 25th ACM …, 2019 – dl.acm.org
Page 1. A Deep Generative Approach to Search Extrapolation and Recommendation Fred X. Han1, Di Niu1, Haolan Chen2 Kunfeng Lai2, Yancheng He2, Yu Xu2 1University of Alberta, Edmonton, AB, Canada 2Platform and Content Group, Tencent, Shenzhen, China …

Asking goal-oriented questions and learning from answers.
A Rothe, BM Lake, TM Gureckis – CogSci, 2019 – nyuccl.org
… Cognitive Science, 42(5), 1410–1456. Du, X., Shao, J., & Cardie, C. (2017). Learning to Ask: Neural Question Generation for Reading Comprehension. arXiv:1705.00106 v1. Graesser, AC, Langston, MC, & Bagget, WB (1993) …

Attentive biLSTMs for Understanding Students’ Learning Experiences
TT Oanh – … on Computer Science, Applied Mathematics and …, 2019 – Springer
… Educ. Assess. Eval. Accountability. 24(2), 99–111 (2012). https://doi.org/10.1007/s11092-011- 9140-4CrossRefGoogle Scholar. 18. Zhao, Y., Ni, X., Ding, Y., Ke, Q.: Paragraph-level neural question generation with maxout pointer and gated self-attention networks …

Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives
L Fu, JP Chang, C Danescu-Niculescu-Mizil – arXiv preprint arXiv …, 2019 – arxiv.org
Page 1. Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives Liye Fu Cornell University liye@cs.cornell.edu Jonathan P. Chang Cornell University jpc362@cornell.edu Cristian Danescu-Niculescu …

Chemical–protein interaction extraction via contextualized word representations and multihead attention
Y Zhang, H Lin, Z Yang, J Wang, Y Sun – Database, 2019 – academic.oup.com
Abstract. A rich source of chemical–protein interactions (CPIs) is locked in the exponentially growing biomedical literature. Automatic extraction of CPIs is a.

BertNet: Combining BERT language representation with attention and CNN for reading comprehension
G Limaye, M Pandit, V Sawal – pdfs.semanticscholar.org
… comprehension. CoRR, abs/1804.09541, 2018. [11] Qingyu Zhou, Nan Yang, Furu Wei, Chuanqi Tan, Hangbo Bao, and Ming Zhou. Neural question generation from text: A preliminary study. CoRR, abs/1704.01792, 2017. 5 …

Using Automatic Item Generation to Create Content for Computerized Formative Assessment
Z Xinxin – 2019 – era.library.ualberta.ca
Page 1. Using Automatic Item Generation to Create Content for Computerized Formative Assessment by Xinxin Zhang A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Measurement, Evaluation and Cognition …

Dynamic Graph Embedding via LSTM History Tracking
S Khoshraftar, S Mahdavi, A An… – 2019 IEEE International …, 2019 – ieeexplore.ieee.org
Page 1. Dynamic Graph Embedding via LSTM History Tracking Shima Khoshraftar ? , Sedigheh Mahdavi ? , Aijun An ? , Yonggang Hu † and Junfeng Liu † ? Electrical Engineering and Computer Science Department York …

BERTQA–Attention on Steroids
A Chadha, R Sood – arXiv preprint arXiv:1912.10435, 2019 – arxiv.org
… [13] Huang et al, FlowQA: Grasping Flow in History for Conversational Machine Comprehension, https://arxiv.org/abs/1810.06683 [14] Neural Question Generation from Text: A Preliminary Study, https://arxiv.org/abs/1704.01792 …

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
A Korhonen, D Traum, L Màrquez – … of the 57th Annual Meeting of the …, 2019 – aclweb.org
Page 1. ACL 2019 The 57th Annual Meeting of the Association for Computational Linguistics Proceedings of the Conference July 28 – August 2, 2019 Florence, Italy Page 2. Diamond Sponsors: Platinum Sponsors: ii Page 3. Gold sponsors: Keiosk Analytics Silver sponsors …

Engaging Audiences in Virtual Museums by Interactively Prompting Guiding Questions
Z Zhao – arXiv preprint arXiv:1902.03527, 2019 – arxiv.org
Page 1. Engaging Audiences in Virtual Museums by Interactively Prompting Guiding Questions Zhenjie Zhao Hong Kong University of Science and Technology Hong Kong zzhaoao@cse.ust. hk ABSTRACT Virtual museums aim to promote access to cultural artifacts …