Teaching Machines to Ask Questions
Open accessAbstract
We propose a novel neural network model that aims to generate diverse and human-like natural language questions. Our model not only directly captures the variability in possible questions by using a latent variable, but also generates certain types of questions by introducing an additional observed variable. We deploy our model in the generative adversarial network (GAN) framework and modify the discriminator which not only allows evaluating the question authenticity, but predicts the question type. Our model is trained and evaluated on a question-answering dataset SQuAD, and the experimental results shown the proposed model is able to generate diverse and readable questions with the specific attribute.
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The model of [40] outperformed baselines [43] and [44] on BLEU, METEOR, ROUGE, and human evaluation.
“Automatic evaluation on BLEU, METEOR, and ROUGE scores was performed along with human-based evaluation and their proposed model outperforms the baselines considered in both types of evaluation.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [40]
Reference [40] incorporates variability and question type prediction into a GAN framework.
“The introduction of variability in the questions generated and prediction of question type is incorporated in a GAN framework discussed in [40].”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [40]
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Cites
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213 · see citing works
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172 · see citing works
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382 · see citing works
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208 · see citing works
(1999) · Cited by 5,147 · see citing works
Get To The Point: Summarization with Pointer-Generator Networks
See, A., Liu, P. J., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1099 · Cited by 3,775 · see citing works
Stochastic Backpropagation and Approximate Inference in Deep Generative ModelsPreprint
Rezende, D. J., Mohamed, S., & Wierstra, D. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1401.4082 · Cited by 3,679 · see citing works
Conditional Image Synthesis With Auxiliary Classifier GANsPreprint
Odena, A., Olah, C., & Shlens, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09585 · Cited by 2,968 · see citing works
Generating Sequences With Recurrent Neural Networks
Graves, A. (2013) · arXiv · doi:10.48550/arxiv.1308.0850 · Cited by 2,894 · see citing works
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Yu, L., Zhang, W., Wang, Y.-X., & Yu, Y. H. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10804 · Cited by 2,342 · see citing works
Generating Sentences from a Continuous Space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A., Jozefowicz, R., & Bengio, S. (2016) · Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1002 · Cited by 2,061 · see citing works
Microsoft COCO Captions: Data Collection and Evaluation Server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollar, P., & Zitnick, C. L. (2015) · arXiv · doi:10.48550/arxiv.1504.00325 · Cited by 1,614 · see citing works
Adversarial Learning for Neural Dialogue Generation
Li, J., Monroe, W., Shi, T., Jean, S., Ritter, A., & Jurafsky, D. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1230 · Cited by 880 · see citing works
Zhao, T., Zhao, R., & Eskenazi, M. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1061 · Cited by 736 · see citing works
Learning to Ask: Neural Question Generation for Reading Comprehension
Du, X., Shao, J., & Cardie, C. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1123 · Cited by 696 · cited 1× · see citing works
“More recently, neural networks based question generation models aim at training an end-to-end system to generate natural language questions from text without human-designed transformation and generation rules [Zhou et al. , 2017; Yuan et al. , 2017; Du et al. , 2017].”
BackgroundGood Question! Statistical Ranking for Question Generation
Heilman, M., & Smith, N. A. (2018) · Figshare · doi:10.1184/r1/6473390 · Cited by 366 · see citing works
Neural Question Generation from Text: A Preliminary Study
Zhou, Q. S., Yang, N., Wei, F., Tan, C., Bao, H., & Zhou, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-73618-1_56 · Cited by 353 · see citing works
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Serban, I. V., García-Durán, A., Gulcehre, C., Ahn, S., Chandar, S., Courville, A., & Bengio, Y. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1056 · Cited by 280 · see citing works
Automatic factual question generation from text
Smith, N. A., & Heilman, M. (2011) · Cited by 168 · see citing works
Machine Comprehension by Text-to-Text Neural Question Generation
Yuan, X., Wang, T., Gülçehre, Ç., Sordoni, A., Bachman, P., Zhang, S., Subramanian, S., & Trischler, A. (2017) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w17-2603 · Cited by 158 · see citing works
Learning through Dialogue Interactions by Asking QuestionsPreprint
Li, J., Miller, A., Chopra, S., Ranzato, M., & Weston, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.04936 · Cited by 74 · see citing works
Controllable Text Generation.Preprint
Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R., & Xing, E. P. (2017) · arXiv (Cornell University) · Cited by 73 · see citing works
Flagged first, then by citations
2 of the 29 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- [Chali and Hasan, 2015] Yllias Chali and Sadid A. Hasan. Towards topic-to-question generation. Computational Linguistics , 41(1):1-20, 2015.
- [Colby et al. , 1971] Kenneth Mark Colby, Sylvia Weber, and Franklin Dennis Hilf. Artificial paranoia. Artif. Intell. , 2(1):1-25, 1971.
- [Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jurgen Schmidhuber. Long short-term memory. Neural Computation , 9(8):1735-1780, 1997.
- [Sohn et al. , 2015] Kihyuk Sohn, Honglak Lee, and Xinchen Yan. Learning structured output representation using deep conditional generative models. In NIPS , 2015.
Cited by
Sorted by citations
Just Ask: Learning to Answer Questions from Millions of Narrated Videos
Yang, A., Miech, A., Šivic, J., Laptev, I., & Schmid, C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00171 · Cited by 220 · see citing works
“Moreover, rule-based generation requires the manual creation of rules by experts which is expensive, and has also been recently outperformed by neural question generation [20, 89, 99] as used in our approach.”
Mulla, N., & Gharpure, P. (2023) · Progress in Artificial Intelligence · doi:10.1007/s13748-023-00295-9 · Cited by 132 · see citing works
“The introduction of variability in the questions generated and prediction of question type is incorporated in a GAN framework discussed in [40].”
MethodLearning to Generate Questions by LearningWhat not to Generate
Liu, B., Zhao, M. M., Niu, D., Lai, K., He, Y., Wei, H., & Xu, Y. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313737 · Cited by 104 · see citing works
“the question. •SeqCopyNet [43] proposed a method to improve the copying mechanism in Seq2Seq models by copying not only a single word but a sequence of words from the input sentence. •seq2seq+z+c+GAN [37] proposed a model employed in GAN framework using the latent variable to capture the diversity and learning disenta…”
MethodA Review on Question Generation from Natural Language Text
Zhang, R., Guo, J., Chen, L., Fan, Y., & Cheng, X. (2021) · ACM Transactions on Information Systems · doi:10.1145/3468889 · Cited by 91 · see citing works
“Similarly, Yao et al. [243] modified the discriminator in vanilla GAN, which not only evaluates the question authenticity, but distinguishes the types of questions, to generate diverse questions.”
BackgroundLee, D. B., Lee, S., Jeong, W. T., Kim, D., & Hwang, S. J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.20 · Cited by 64 · see citing works
“kled by only a few recent works (Du and Cardie,2018;Alberti et al.,2019;Dong et al.,2019). To the best of our knowledge, we are the first to propose a probabilistic generative model for end-to-end QAG;Yao et al. (2018) use VAE for QG, but they do not tackle QAG. Moreover, we effectively resolve the QApair consistency i…”
BackgroundLet’s Ask Again: Refine Network for Automatic Question Generation
Nema, P., Mohankumar, A. K., Khapra, M. M., Srinivasan, B. V., & Ravindran, B. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1326 · Cited by 57 · see citing works
“Current models for AQG are based on the encode-attend-decode paradigm and they either generate questions from the passage alone (Du and Cardie, 2017; Du et al., 2017; Yao et al., 2018) or from the passage and a given answer (in which case the generated question must result in the given answer).”
MethodLearning to Ask Unanswerable Questions for Machine Reading Comprehension
Zhu, H., Dong, L., Wei, F., Wang, W., Qin, B., & Liu, T. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1415 · Cited by 45 · see citing works
“Yao et al. (2018) introduce a latent variable for capturing variability and an observed variable for controlling question types.”
BackgroundMulti-hop Question Generation with Graph Convolutional Network
Su, D., Xu, Y., Dai, W., Ji, Z., Yu, T., & Fung, P. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.416 · Cited by 38 · see citing works
Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text
Kumar, V., Ramakrishnan, G., & Li, Y.-F. (2019) · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) · doi:10.18653/v1/k19-1076 · Cited by 46 · see citing works
“(Yao et al., 2018) proposed generative adversarial network (GAN) framework with modified discriminator to predict question type.”
BackgroundMavi, V., Jangra, A., & Jatowt, A. (2024) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000102 · Cited by 79 · see citing works
“Mathematical word problem generation from commonsense knowledge graph and equations,”
Liu, T., Fang, Q., Ding, W., Li, H., Wu, Z., & Liu, Z. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.348 · Cited by 29 · see citing works
Learning to Answer Visual Questions From Web Videos
Yang, A., Miech, A., Šivic, J., Ivan Laptev, A., & Schmid, C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3173208 · Cited by 27 · see citing works
“Moreover, rule-based generation requires the manual creation of rules by experts which is expensive, and has also been recently outperformed by neural question generation [26], [107], [119] as used in our approach.”
Exploring Question-Specific Rewards for Generating Deep Questions
Xie, Y., Pan, L., Wang, D., Kan, M., & Feng, Y. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.228 · Cited by 27 · see citing works
“Other types of rewards include Yao et al. (2018), which train a discriminator to measure the naturalness , i.e. , the question is human-written or generated.”
BackgroundChai, Z., & Wan, X. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.21 · Cited by 23 · see citing works
“In addition, enhancing the Seq2seq model into more complicated structures using variational inference, adversarial training and reinforcement learning (Yao et al., 2018; Kumar et al., 2019) have also gained much attention.”
BackgroundQu, F., Jia, X., & Wu, Y. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.202 · Cited by 30 · see citing works
“Yao et al. (2018) present a Seq2Seq model deployed in GAN with a discriminator, which encourages model to generate more readable and diverse questions with the certain types.”
MethodSelf-citationDiversify Question Generation with Continuous Content Selectors and Question Type Modeling
Wang, Z., Rao, S., Zhang, J., Qin, Z., Tian, G., & Wang, Y.-X. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.194 · Cited by 16 · see citing works
“Yao et al. (2018) used a latent variable to model the holistic attributes in questions.”
BackgroundMethodOpen-domain clarification question generation without question examples
White, J. A., Poesia, G., Hawkins, R., Sadigh, D., & Goodman, N. D. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.44 · Cited by 15 · see citing works
“One popular approach is to train an end-to-end model to map visual and linguistic inputs directly to questions (Yao et al., 2018; Das et al., 2017).”
BackgroundLearning visual language models for video understanding
Yang, A. (2023) · theses.fr (ABES) · doi:10.70675/802872a7z8092z4556za1fdz2c7af45f0e72 · Cited by 15 · see citing works
Shahidi, H., Li, M., & Lin, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.355 · Cited by 13 · see citing works
Natural Question Generation using Transformers and Reinforcement Learning
Biswas, D., Nadipalli, S., Sneha, B., Gupta, D., & Amudha, J. (2022) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit56763.2022.00061 · Cited by 12 · see citing works
“In line with this method, comes another approach, this time harnessing the power of GANs [14].”
MethodImproving Neural Question Generation using Deep Linguistic Representation
Yuan, W., He, T., & Dai, X. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449975 · Cited by 10 · see citing works
“[56] deploys a model in Generative Adversarial Network (GAN) and modify the discriminator for evaluating question authenticity and predicting question type.”
MethodMulla, N., & Gharpure, P. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01262-2 · Cited by 10 · see citing works
Engage the Public: Poll Question Generation for Social Media Posts
Lu, Z., Ding, K., Zhang, Y., Li, J., Peng, B., & Liu, L. (2021) · doi:10.18653/v1/2021.acl-long.3 · Cited by 10 · see citing works
“Despite of the extensive efforts made in this line (Sun et al., 2018; Yao et al., 2018; Chai and Wan, 2020; Sun et al., 2020), most previous work focus on the processing of formally-written texts, such as exam questions in reading comprehension tests.”
BackgroundQA4QG: Using Question Answering to Constrain Multi-Hop Question Generation
Su, D., Xu, P., & Fung, P. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747008 · Cited by 10 · see citing works
Shinoda, K., Sugawara, S., & Aizawa, A. (2021) · doi:10.18653/v1/2021.acl-srw.21 · Cited by 9 · see citing works
“To further improve question quality, policy gradient techniques have been used (Yuan et al., 2017; Yang et al., 2017a; Yao et al., 2018; Kumar et al., 2019).”
BackgroundMethodInfluentialHACAN: a hierarchical answer-aware and context-aware network for question generation
Sun, R., Tao, H., Chen, Y., & Liu, Q. (2023) · Frontiers of Computer Science · doi:10.1007/s11704-023-2246-2 · Cited by 8 · see citing works
Capturing Greater Context for Question Generation
Tuan, L. A., Shah, D., & Barzilay, R. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6440 · Cited by 7 · see citing works
Keeping Notes: Conditional Natural Language Generation with a Scratchpad Encoder
Benmalek, R. Y., Khabsa, M., Desu, S., Cardie, C., & Banko, M. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1407 · Cited by 7 · see citing works
“…question generation domain, there has been a recent surge in research on generating questions for a given paragraph of text (Song et al., 2017; Du et al., 2017; Tang et al., 2017; Duan et al., 2017; Wang et al., 2018; Yao et al., 2018), with most of the work being a variant of the seq2seq approach.”
BackgroundWang, Q., Yang, L., Quan, X., Feng, F., Liu, D., Xu, Z., Wang, S., & Ma, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.4 · Cited by 6 · see citing works
“Several AQG models have been proposed to generate questions from the passage alone (Du and Cardie, 2017; Yao et al., 2018).”
BackgroundQuestion-Worthy Sentence Selection for Question Generation
Mahdavi, S., An, A., Davoudi, H., Delpisheh, M., & Boroujerdi, E. G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-47358-7_40 · Cited by 5 · see citing works
Let's Ask Again: Refine Network for Automatic Question GenerationPreprint
Nema, P., Mohankumar, A. K., Khapra, M. M., Srinivasan, B. V., & Ravindran, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05355 · Cited by 4 · see citing works
“Current models for AQG are based on the encode-attend-decode paradigm and they either generate questions from the passage alone (Du and Cardie, 2017; Du et al., 2017; Yao et al., 2018) or from the passage and a given answer (in which case the generated question must result in the given answer).”
MethodShinoda, K., Sugawara, S., & Aizawa, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.03238 · Cited by 3 · see citing works
“To further improve question quality, policy gradient techniques have been used (Yuan et al., 2017; Yang et al., 2017a; Yao et al., 2018; Kumar et al., 2019).”
BackgroundMethodInfluentialEvaluating for Diversity in Question Generation over TextPreprint
Schlichtkrull, M., & Cheng, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.07291 · Cited by 2 · see citing works
“C L ] 1 7 A ug 2 02 0 2017; Harrison and Walker, 2018; Song et al., 2018; Yao et al., 2018; Shen et al., 2020) attempting to improve semantic diversity by conditioning on (potential) answer positions or question types.”
BackgroundQu, F., Jia, X., & Wu, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05179 · Cited by 4 · see citing works
“Yao et al. (2018) present a Seq2Seq model deployed in GAN with a discriminator, which encourages model to generate more readable and diverse questions with the certain types.”
MethodSelf-citationKeeping Notes: Conditional Natural Language Generation with a Scratchpad MechanismPreprint
Benmalek, R. Y., Khabsa, M., Desu, S., Cardie, C., & Banko, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05275 · Cited by 1 · see citing works
“…question generation domain, there has been a recent surge in research on generating questions for a given paragraph of text (Song et al., 2017; Du et al., 2017; Tang et al., 2017; Duan et al., 2017; Wang et al., 2018; Yao et al., 2018), with most of the work being a variant of the seq2seq approach.”
BackgroundAsking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums
Chai, Z., Xing, X., Wan, X., & Huang, B. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1497
“Recently, using adversarial training and reinforcement learning (Yuan et al., 2017; Kumar et al., 2018b; Yao et al., 2018) have become a new trend.”
BackgroundAutomatic Question and Answer Generation from Text Using Neural Networks
Soni, S., Kumar, P., & Saha, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-15-9774-9_46
Semantic Templates for Generating Long-Form Technical Questions
Pal, S., Singh, A., Datta, S., Patil, S., Bhattacharya, I., & Palshikar, G. K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-83527-9_20
Open-domain clarification question generation without question examplesPreprint
White, J. A., Poesia, G., Hawkins, R., Sadigh, D., & Goodman, N. D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.09779
“One popular approach is to train an end-to-end model to map visual and linguistic inputs directly to questions (Yao et al., 2018; Das et al., 2017).”
BackgroundSemantic Representation Based on AMR Graph for Document Level Question Generation
Cheng, S., Chen, J., & Yu, Y. (2022) · International Conference on Machine Learning and Natural Language Processing (MLNLP) · doi:10.1145/3578741.3578762
“al [26] presents a novel model deployed in the Generative Adversarial Network (GAN) and variational auto-encoders (VAE).”
BackgroundDeep question generation model based on dual attention guidance
Li, J., Zhang, X., Wang, J., & Zhou, X. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02249-6
First Question Generation Method via Summarization and Syntax for English Reading Tests
张, 懿. (2024) · Computer Science and Application · doi:10.12677/csa.2024.148178
- Authors
- Yao, K., Zhang, L., Luo, T., Tao, L., & Wu, Y.
- Affiliations
- Chinese Academy of Sciences (CN)
- University of the West of England (GB)
- Venue
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Published
- 2018-07
- Pages
- 4546-4552
- OpenAlex
- W2807750936
- Cited by
- 44
- References
- 29
- Citations by year
- 2024: 3 · 2023: 4 · 2022: 5 · 2021: 13 · 2020: 11 · 2019: 8
- FWCI
- 3.25
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- UWE Research Repository (UWE Bristol) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- question generation, latent variable models, generative adversarial networks, SQuAD, question answering dataset
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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