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Learning to Ask: Neural Question Generation for Reading Comprehension
Open accessAbstract
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 sequenceto-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).
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In the citing paper
“The authors performed both automatic evaluations using BLEU, METEOR, and ROUGE as well as human-based evaluation and found that their model outperformed the baseline model in [32] that they considered.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [32]
In the citing paper
“Other approaches make use of naturalness [32, 35] and difficulty [35].”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [32]
In the citing paper
“However, the questions generated lack to a certain extent in terms of semantics and relevance as reported in most studies through human evaluation [32, 35, 36, 39, 56, 61].”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [32]
In the citing paper
“While ITSs have traditionally been defined and constructed by hand, recent approaches have applied ML techniques such as multi-armed bandit techniques to adaptively personalize sequences of learning activities [135], LSTMs to generate questions to evaluate language comprehension [205], and RL to improve the strategies used within the ITS [365,441].”
From Tackling climate change with machine learning (2023) · cites this paper as [205]
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · cited 2×
“W s and W t are parameters to be learned. here, LSTM is the Long Short-Term Memory (LSTM) network (Hochreiter and Schmidhuber, 1997).”
BackgroundGlove: 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 · cited 2×
“300d pre-trained embeddings (Pennington et al., 2014) for initialization.”
MethodLearning 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 · cited 2×
“We model the conditional probability us-ing RNN encoder-decoder architecture (Bahdanau et al., 2015; Cho et al., 2014), and adopt the global attention mechanism (Luong et al., 2015a) to make the model focus on certain elements of the input when generating each word during decoding.”
MethodBLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344 · cited 2×
“The package includes BLEU 1, BLEU 2, BLEU 3, BLEU 4 (Papineni et al., 2002), METEOR (Denkowski and Lavie, 2014) and ROUGE L (Lin, 2004) evaluation scripts.”
MethodSequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · cited 4×
“More specifically, inspired by the recent success in neural machine translation (Sutskever et al., 2014; Bahdanau et al., 2015), summarization (Rush et al.”
BackgroundInfluentialNeural 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 · cited 4×
“We model the conditional probability us-ing RNN encoder-decoder architecture (Bahdanau et al., 2015; Cho et al., 2014), and adopt the global attention mechanism (Luong et al., 2015a) to make the model focus on certain elements of the input when generating each word during decoding.”
BackgroundMethodInfluentialBinary Codes capable of currecting deletions, insertions, and reversals
Levenshtein, V. I. (1965) · Soviet physics. Doklady · Cited by 10,387
(2004) · Cited by 9,718
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 · cited 3×
“We model the conditional probability us-ing RNN encoder-decoder architecture (Bahdanau et al., 2015; Cho et al., 2014), and adopt the global attention mechanism (Luong et al., 2015a) to make the model focus on certain elements of the input when generating each word during decoding.”
MethodShow, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · cited 2×
“, 2016), and image caption generation (Xu et al., 2015), we tackle question generation using a conditional neural language model with a global attention mechanism (Luong et al.”
Method
Research works printed but not linked: 2
- Thang Luong, Hieu Pham, and Christopher D. Manning. 2015a. Effective approaches to attentionbased neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Lisbon, Portugal, pages 14121421. http://aclweb.org/anthology/D15-1166.
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Items 301–400 of 695
Generating Questions for Reading Comprehension using Coherence Relations
Desai, T., Dakle, P. P., & MOLDOVAN, D. I. (2018) · Workshop on Natural Language Processing Techniques for Educational Applications (NLP-TEA) · doi:10.18653/v1/w18-3701 · Cited by 10
“Yin et al. (2015) and Du et al. (2017) argue that similar models can be used to automatically translate narrative sentences into interroga-tive ones.”
BackgroundParaQG: A System for Generating Questions and Answers from Paragraphs
Kumar, V., Muneeswaran, S., Ramakrishnan, G., & Li, Y.-F. (2019) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/d19-3030 · Cited by 10
“Most of these models (Du et al., 2017; Kumar et al., 2018; Song et al., 2018; Kumar et al., 2019c,b) take single sentence as input, thus limiting their usefulness in real-world settings.”
BackgroundAccelerating Real-Time Question Answering via Question GenerationPreprint
Fang, Y., Wang, S., Gan, Z., Sun, S., Liu, J., & Zhu, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05167 · Cited by 17
“To encourage more organic integration of Question Generation (QG) (Rus et al., 2008; Du et al., 2017; Zhang and Bansal, 2019; Dong et al., 2019) into QA systems, besides the QG evaluation metrics of BLEU (Papineni et al., 2002), ROUGE (Lin, 2004) and METEOR (Lavie and Denkowski, 2009), we also…”
BackgroundMethodWhat BERT Sees: Cross-Modal Transfer for Visual Question Generation
Scialom, T., Bordes, P., Dray, P.-A., Staiano, J., & Gallinari, P. (2020) · Proceedings of the 13th International Conference on Natural Language Generation · doi:10.18653/v1/2020.inlg-1.39 · Cited by 9
“Metrics were computed using the Python package released by Du et al. (2017).6”
MethodImproving Question Generation with Multi-level Content Planning
Xia, Z., Gou, Q., Yu, B., Yu, H., Huang, F., Li, Y., & Nguyen, C.-T. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.57 · Cited by 13
Evaluating the performance of multilingual models in answer extraction and question generation
Moreno-Cediel, A., del-Hoyo-Gabaldon, J.-A., García‐López, E., García‐Cabot, A., & de-Fitero-Dominguez, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-66472-5 · Cited by 13
Kaffee, L.-A., Vougiouklis, P., & Simperl, E. P. B. (2021) · Semantic Web · doi:10.3233/sw-210431 · Cited by 12
“Our NLG solution builds upon the general encoderdecoder framework for neural networks, which is credited with promising results in similar text-centric tasks, such as machine translation [12,82] and question generation [16,19,78].”
Difficulty-Controllable Visual Question Generation
Chen, F., Xie, J., Cai, Y., Wang, T., & Li, Q. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85896-4_26 · Cited by 9
Graph augmented sequence-to-sequence model for neural question generation
Ma, H., Wang, J., Lin, H., & Xu, B. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04260-2 · Cited by 9
“NQG is further classified into two streams: answer-aware [9–11] and answer-agnostic [8, 14].”
BackgroundAutomatic Question Generation based on MOOC Video Subtitles and Knowledge Graph
Ma, L., & Ma, Y. (2019) · International Conference on Information and Education Technology (ICIET) · doi:10.1145/3323771.3323820 · Cited by 9
A Virtual Dialogue Assistant for Conducting Remote Exams
Matveev, A., Makhnytkina, O. V., Lizunova, I. A., Vinogradova, T., Chirkovskiy, A., Svischev, A., & Mamaev, N. (2020) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct48808.2020.9087557 · Cited by 9
“For example, Du et al. [2] use an encoder-decoder network to create factual questions based on the SQuAD corpus in an end-to-end fashion.”
MethodWu, X., Jiang, N., & Wu, Y. (2020) · Workshop on Neural Generation and Translation (WNGT) · doi:10.18653/v1/2020.ngt-1.8 · Cited by 10
Automatic Question Generation System for English Reading Comprehension
Fung, Y.-C., Kwok, J. C.-W., Lee, L., Chui, K. T., & Leong Hou, U. (2020) · Communications in computer and information science · doi:10.1007/978-981-33-4594-2_12 · Cited by 11
AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph Documents
Roemmele, M., Sidhpura, D., DeNeefe, S., & Tsou, L. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-demos.6 · Cited by 12
Shinoda, K., Sugawara, S., & Aizawa, A. (2021) · doi:10.18653/v1/2021.acl-srw.21 · Cited by 10
“Since the SQuAD test set has not been released, we use the split of the dataset, SQuAD-Du (Du et al., 2017”
MethodWeakly-Supervised Questions for Zero-Shot Relation Extraction
Najafi, S., & Fyshe, A. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.224 · Cited by 9
Generating Highly Relevant Questions
Qiu, J., & Xiong, D. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1614 · Cited by 9
“Specifically, the encoder-decoder seq2seq model (Du et al., 2017; Zhou et al., 2017; Song et al., 2018) is used to encode a passage and generate a question corresponding to the answer.”
InfluentialNatural Language Generation with Neural Variational ModelsPreprint
Bahuleyan, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.09012 · Cited by 10
“Neural networks were implemented for this task only recently by Du et al. (2017) and Zhou et al. (2017).”
BackgroundMethodInfluentialTowards Automatic Generation of Questions from Long AnswersPreprint
Mishra, S. K., Goel, P., Sharma, A., Jagannatha, A., Jacobs, D. W., & Daumé, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.05109 · Cited by 11
“It’s in contrast with the previous approaches (Zhao et al., 2018; Du et al., 2017) that use the answer-containing paragraph as additional contextual information for short answer question generation.”
BackgroundMethodResultInfluentialGLGE: A New General Language Generation Evaluation BenchmarkPreprint
Liu, D., Yan, Y., Gong, Y., Qi, W., Zhang, H., Jiao, J., Chen, W., Fu, J., Shou, L., Gong, M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.11928 · Cited by 25
“Since the original hidden test set of the SQuAD 1.1 is hidden, we follow (Du et al., 2017; Zhao et al., 2018) to re-split the dataset with the examples from the original training set and development set.”
MethodInfluentialBiomedical Question Answering: A Comprehensive Review.Preprint
Jin, Q., Yuan, Z. P., Xiong, G., Yu, Q., Tan, C., Chen, M., Huang, S., Liu, X., & Yu, S. (2021) · arXiv (Cornell University) · Cited by 9
Enhancing Question Prediction with Flan T5 -A Context-Aware Language Model ApproachPreprint
Oza, J., & Yadav, H. S. (2023) · doi:10.22541/au.170258918.81486619/v1 · Cited by 9
Piryani, B., Mozafari, J., & Jatowt, A. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657891 · Cited by 15
“The final dataset that we release comprises 485k question-answer pairs spanning 120 years (1800-1920) .”
Yuan, M., Kao, B., Wu, T.-H., Cheung, M. M. K., Chan, H. W. H., Cheung, A. S. Y., Chan, F. W. H., & Chen, Y. (2023) · Artificial Intelligence and Law · doi:10.1007/s10506-023-09367-6 · Cited by 8
“3.4).”
InfluentialGeneration of Multiple Choice Questions from Indian Educational Text
Saddish, A., Somaiah, P., Gambhir, V., & Nirmala, S. J. (2023) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon58793.2023.10270551 · Cited by 8
“This results in the average fluency score being 4.39 as shown in Figure 2, which indicates better performance than that of traditional SLM and a transformer based model as seen in Table II.”
MethodInfluentialDiversify Question Generation with Retrieval-Augmented Style Transfer
Gou, Q., Xia, Z., Yu, B., Yu, H., Huang, F., Li, Y., & Nguyen, C.-T. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.104 · Cited by 12
HACAN: 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 9
Answer Agnostic Question Generation in Bangla Language
Fahad, A. R., Nahian, N. A., Islam, M. A., & Rahman, R. M. (2024) · The International journal of networked and distributed computing · doi:10.1007/s44227-023-00018-5 · Cited by 8
“In 2017, Du et al. [19] sought to generate questions for reading comprehension, they were the first to use a sequence-to-sequence approach to produce questions.”
RTRL: Relation-aware Transformer with Reinforcement Learning for Deep Question Generation
Zeng, H., Wei, B., & Liu, J. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112120 · Cited by 9
Toward Stance-based Personas for Opinionated Dialogues
Scialom, T., Tekiroğlu, S. S., Staiano, J., & Guerini, M. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.238 · Cited by 8
Answer-Aware Question Generation from Tabular and Textual Data using T5
Pandraju, S., & Mahalingam, S. G. (2021) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v16i18.25121 · Cited by 10
“To alleviate this problem and create more training data, there is a growing interest in developing new techniques that can automatically generate questions from a given source like a document [9] [10].”
BackgroundMethodTowards Multilingual Neural Question Answering
Loginova, E., Varanasi, S., & Neumann, G. (2018) · Communications in computer and information science · doi:10.1007/978-3-030-00063-9_26 · Cited by 8
“The recent success of deep learning question generation [12] is promising for doing so in an end-to-end (semi-) automated fashion, which is important for languages with scarce resources.”
BackgroundAddressing Semantic Drift in Question Generation for Semi-Supervised Question AnsweringPreprint
Zhang, S., & Bansal, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.06356 · Cited by 19
“, 2014) for QG tasks, taking answer sentence as input and outputting the question (Du et al., 2017; Zhou et al., 2017), which proved to be better than rulebased methods.”
BackgroundMethodInfluentialColdGANs: Taming Language GANs with Cautious Sampling StrategiesPreprint
Scialom, T., Dray, P.-A., Lamprier, S., Piwowarski, B., & Staiano, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04643 · Cited by 8
“Following previous works [9, 10], we used the SQuAD dataset [33].”
MethodDiversify Question Generation with Retrieval-Augmented Style Transfer
Gou, Q., Xia, Z., Yu, B., Yu, H., Huang, F., Li, Y., & Cam-Tu, N. (2023) · arXiv · doi:10.48550/arxiv.2310.14503 · Cited by 8
“…the model may degenerate to a locally optimal one, during which the retrieval puts all the probability mass on a small number of templates very close to z 0 according to surface matching, ignoring all (Zhou et al., 2017), and SQuAD /2 is another split from (Du et al., 2017). the other templates.”
BackgroundMethodInfluentialAttacking Open-domain Question Answering by Injecting Misinformation
Pan, L., Chen, W., Kan, M.-Y., & Wang, W. Y. (2023) · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.ijcnlp-main.35 · Cited by 9
“A possible reason is that most questions in SQuAD are shallow in reasoning (Du et al., 2017).”
BackgroundHandover QG: Question Generation by Decoder Fusion and Reinforcement Learning
Chung, H.-L., Chan, Y.-H., & Fan, Y.-C. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3426292 · Cited by 8
Wang, Z., Zhang, Y., Min, W., Guan, Q., & Yu, W. (2025) · Journal of Geography in Higher Education · doi:10.1080/03098265.2025.2549784 · Cited by 9
“Many approaches continue to emerge, such as encoder-decoder frameworks (Du et al., 2017; Zhou et al., 2018), generative adversarial networks (Rao & Daumé Iii, 2019; Yao et al., 2018), deep reinforcement learning architectures (Chen et al., 2019; Fan et al., 2018; Kumar et al., 2018), and…”
Queries and Cues: Textual Stimuli for Reflective Thinking in Digital Mind-Mapping
Chen, T., Mohanty, R. R., & Krishnamurthy, V. R. (2021) · Journal of Mechanical Design · doi:10.1115/1.4052297 · Cited by 7
Shinoda, K., Sugawara, S., & Aizawa, A. (2021) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/2021.mrqa-1.6 · Cited by 9
Revisiting paraphrase question generator using pairwise discriminator
Patro, B. N., Chauhan, D., Kurmi, V. K., & Namboodiri, V. P. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.08.022 · Cited by 7
Question Generation Through Transfer Learning
Liao, Y.-H., & Koh, J.-L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-55789-8_1 · Cited by 7
Zero-shot Multi-lingual Interrogative Question Generation for "People Also Ask" at Bing
Mitra, R., Jain, R., Veerubhotla, A. S., & Gupta, M. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3469403 · Cited by 7
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
“…the 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…”
BackgroundSelf-citationExploring Artificial Jabbering for Automatic Text Comprehension Question Generation
Steuer, T., Filighera, A., & Rensing, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57717-9_1 · Cited by 8
Weak Supervision Enhanced Generative Network for Question Generation
Wang, Y., Zheng, J., Liu, Q., Zhao, Z., Xiao, J., & Zhuang, Y. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/528 · Cited by 7
A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature
Bhambhoria, R. V., Feng, L. Y., Sepehr, D., Chen, J., Cowling, C., Koçak, S. A., & Dolatabadi, E. (2020) · Workshop on Scholarly Document Processing (SDP) · doi:10.18653/v1/2020.sdp-1.4 · Cited by 7
“We limit our question generation procedure to trans-former and rule-based methods, refraining from the usage of other neural question generation meth-ods such as (Du et al., 2017; Krishna and Iyyer, 2019) in order to create simple baselines for the comparison and evaluation of subsequent sections.”
MethodFAQ-Gen: An automated system to generate domain-specific FAQs to aid content comprehension
Kale, S., Khaire, G., & Patankar, J. (2024) · Journal of Computer-Assisted Linguistic Research · doi:10.4995/jclr.2024.21178 · Cited by 9
“…on specific datasets, requires adaptation for new domains and document styles Learning to Ask: Neural Question Generation for Reading Comprehension [24] Introduces an attention-based model for generating high-quality, diverse questions directly from text passages, satisfying the requirements of…”
Machine-Generated Questions Attract Instructors When Acquainted with Learning Objectives
Shimmei, M., Bier, N., & Matsuda, N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36272-9_1 · Cited by 9
Fan, A. X., Zhang, H., Paquette, L., & Zhang, R. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.496 · Cited by 12
Last, M., & Danon, G. (2020) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1382 · Cited by 9
“, Heilman, 2011; Kalady, Elikkottil, & Das, 2010) aimed at creating systems for AQG that can take as input a text document and create as output a ranked list of factoid questions. These questions are used to ask for fact-based answers, and usually start with one of these question tools: what, where, when, and who. A u…”
BackgroundMethodInfluentialTowards the benchmarking of question generation: introducing the Monserrate corpus
Rodrigues, H. P., Nyberg, E., & Coheur, L. (2021) · Language Resources and Evaluation · doi:10.1007/s10579-021-09545-5 · Cited by 7
“The second one is the work of Du et al. (2017), from now on D&A, and is a neural network system.”
BackgroundMethodInfluentialUnsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training
Zhu, P., & Hauff, C. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.183 · Cited by 7
AttractionDetailsQA: An Attraction Details Focused on Chinese Question Answering Dataset
Huang, W., Xu, S., Wang, Y., Jin, F., & Chang, Q. (2022) · IEEE Access · doi:10.1109/access.2022.3181188 · Cited by 7
Educational Question Classification with Pre-trained Language Models
Faraby, S. A., Adiwijaya, A., & Romadhony, A. (2022) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic56845.2022.10006957 · Cited by 8
“Much research has been done to generate questions automatically, both rule-based [9], [10] and learning from data especially using neural methods [11], [12].”
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
“Specifically, similar to the metrics used in (Du et al., 2017), we consider two criterion in human evaluation: (1) Naturalness, which indicates the grammaticality and fluency of the generated questions; and (2) Answerability, which measures the correctness of the question, i.e., whether it can…”
BackgroundMethodCONSISTENT: Open-Ended Question Generation From News Articles
Chakrabarty, T., Lewis, J., & Muresan, S. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.517 · Cited by 6
Zelikman, E., Ma, W. A., Tran, J. E., Yang, D., Yeatman, J. D., & Haber, N. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.135 · Cited by 15
Answer-Focused and Position-Aware Neural Network for Transfer Learning in Question Generation
Zi, K., Sun, X., Cao, Y., Wang, S., Feng, X., Ma, Z., & Cao, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29563-9_30 · Cited by 6
Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question Decomposition
Geva, M., Wolfson, T., & Berant, J. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00450 · Cited by 11
“, 2021), and to passage-to-question generation methods (Du et al., 2017; Wang et al., 2020; Duan et al., 2017) that, in contrast to our work, focused on simple questions not requiring reasoning.”
ResultPrabowo, M. W., Budi, I. D., & Santoso, H. B. (2021) · International Conference on Software and Computer Applications (ICSCA) · doi:10.1145/3457784.3457824 · Cited by 6
“Its variant like biLSTM or biGRU is also used in some research in QG [5, 6].”
MethodGenerating Answer Candidates for Quizzes and Answer-Aware Question Generators
Vachev, K., Hardalov, M., Karadzhov, G., Georgiev, G., Releva AI, S., Koychev, I. K., & Nakov, P. (2021) · Student Research Workshop .../Proceedings of the Student Research Workshop ... · doi:10.26615/issn.2603-2821.2021_028 · Cited by 7
Diversity and Consistency: Exploring Visual Question-Answer Pair Generation
Yang, S., Zhou, Q. S., Feng, D., Liu, Y., Li, C., Cao, Y., & Li, D. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.91 · Cited by 6
“Question generation is firstly studied on text (Heilman and Smith, 2010; Labutov et al., 2015; Du et al., 2017; Zhou et al., 2018; Sun et al., 2018; Ma et al., 2020; Kim et al., 2019).”
BackgroundLeveraging Structured Information from a Passage to Generate Questions
Xu, J., Sun, Y., Gan, J., Zhou, M., & Wu, D. (2022) · Tsinghua Science & Technology · doi:10.26599/tst.2022.9010034 · Cited by 6
Joint Generator-Ranker Learning for Natural Language Generation
Shen, W., Gong, Y., Shen, Y., Wang, S., Quan, X., Duan, N., & Chen, W. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.486 · Cited by 6
Yu, J., Wang, S., Zheng, L., Su, Q., Liu, W., Zhao, B., & Yin, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.30 · Cited by 6
“They learned direct mappings from input texts to questions by an encoder-decoder framework (Du et al., 2017).”
BackgroundAsking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives
Fu, L., Chang, J. P., & Danescu-Niculescu-Mizil, C. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1052 · Cited by 9
“(2017) for surveys), generate a question that is answerable from the text (question generation (Du et al., 2017)), or answer a question for which the information can be retrieved or reasoned directly from the story (reading comprehension, see Chen (2018) for a survey; notable datasets include MCTest (Richardson, 2013)…”
BackgroundEvaluating Rewards for Question Generation ModelsPreprint
Hosking, T., & Riedel, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.11049 · Cited by 6
“Such models are trained to generate a plausible question, conditioned on an input document and answer span within that document (Zhou et al., 2018; Du et al., 2017; Du and Cardie, 2018; Yuan et al., 2017).”
BackgroundMethodInfluentialAdversarial Domain Adaptation for Machine Reading ComprehensionPreprint
Wang, H., Gan, Z., Liu, X., Liu, J., Gao, J., & Wang, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.09209 · Cited by 6
“We plan to study if recent question generation methods (Du et al., 2017; Duan et al., 2017; Sun et al., 2018; Benmalek et al., 2019) could further help to close the performance gap in future work.”
BackgroundStronger Transformers for Neural Multi-Hop Question GenerationPreprint
Sachan, D. S., Wu, L., Sachan, M., & Hamilton, W. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11374 · Cited by 7
“Recent work on QG has mostly focused on generating one-hop questions conditioned using neural S2S models (Du et al., 2017), pre-trained transformers (Dong et al., 2019), query reformulation using reinforcement learning (Buck et al., 2018), and reinforcement learning based G2S model (Chen et al.,…”
Simplifying Paragraph-level Question Generation via Transformer Language ModelsPreprint
Lopez, L. E., Cruz, D. K., Cruz, J. C. B., & Cheng, C. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.01107 · Cited by 30
“The most prevalent technique for question generation studies is the usage of a sequence-to-sequence (Seq2Seq) model [4,3,20,2] in addition to a variety of other features and mechanisms.”
BackgroundMethodInfluentialGierl, M. J., Shin, J., & Firoozi, T. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-818630-5.10026-0 · Cited by 7
Diversified generation of commonsense reasoning questions
Yu, J., Wang, S., Yin, H., Chen, Q., Liu, W., Rao, Y., & Su, Q. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125776 · Cited by 5
AI-Powered MicroApps for Online Assessments: Impacts on Efficiency, Quality, and Future Directions
Elhayany, M., Swope, J. A., Rushe, S., & Meinel, C. (2024) · IEEE Digital Education and MOOCS Conference (DEMOcon) · doi:10.1109/democon63027.2024.10748039 · Cited by 5
Kalpakchi, D., & Boye, J. (2023) · Natural Language Engineering · doi:10.1017/s1351324923000037 · Cited by 7
“The final step is referred to as mean filtering .”
BackgroundMethodInfluentialAutomatically Selecting Follow-up Questions for Deficient Bug Reports
Imran, M. M., Ciborowska, A., & Damevski, K. (2021) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr52588.2021.00029 · Cited by 5
“One topic of extensive prior research is on generating questions from an existing document, i.e., questions whose answers can be found within the given text [32]–[37].”
BackgroundNakano, Y., Kawano, S., Yoshino, K., Sudoh, K., & Nakamura, S. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.4 · Cited by 6
“Question generation is also widely researched by using generative models (Duan et al., 2017; Du et al., 2017; Sasazawa et al., 2019) or syntactic rules (Heilman and Smith, 2010).”
BackgroundTowards Diverse and Effective Question-Answer Pair Generation from Children Storybooks
Eo, S., Moon, H., Kim, J., Hur, Y., Kim, J., Lee, S., Chun, C., Park, S. S., & Lim, H. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.380 · Cited by 6
An Arabic question generation system based on a shared BERT-base encoder-decoder architecture
Lafkiar, S., & En-Nahnahi, N. (2024) · Mathematical Modeling and Computing · doi:10.23939/mmc2024.03.763 · Cited by 6
“Some studies utilized sequence-to-sequence models, such as Recurrent Neural Networks (RNNs) [7,8] and Transformer-based models [9,10].”
MethodAutomatically Generating Questions about Novel Metaphors in Literature
Parde, N., & Nielsen, R. D. (2018) · International Conference on Natural Language Generation · doi:10.18653/v1/w18-6533 · Cited by 5
“…systems exist for educational applications, with most generating factoid questions about content found in expository text (Araki et al., 2016; Du et al., 2017; Gates, 2008; Heilman and Smith, 2010; Mazidi and Nielsen, 2014, 2015; Rus et al., 2007; Serban et al., 2016; Wyse and Piwek, 2009).”
BackgroundGenerating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models
Cho, W. S., Zhang, Y., Rao, S., Brockett, C., & Lee, S. (2019) · Workshop on Neural Generation and Translation (WNGT) · doi:10.18653/v1/d19-5604 · Cited by 6
“The use of neural networks to generate natural language questions has mostly focused on question answering (Labutov et al., 2015; Serban et al., 2016; Rothe et al., 2016; Song et al., 2017; Duan et al., 2017; Du et al., 2017; Buck et al., 2017; Song et al., 2018; Harrison and Walker, 2018; Sun et al., 2018).”
BackgroundNeural Question Generation using Interrogative Phrases
Sasazawa, Y., Takase, S., & Okazaki, N. (2019) · International Conference on Natural Language Generation · doi:10.18653/v1/w19-8613 · Cited by 5
“Following other QG studies, we use three evaluation metrics: BLEU, METEOR, and ROUGE L similar to other QG studies (Du et al., 2017; Kim et al., 2018).”
MethodLearning to Generate Diverse Questions from Keywords
Pan, Y., Hu, B., Chen, Q., Xiang, Y., & Wang, X. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053822 · Cited by 5
“Recently, various deep neural networks are widely applied on the question generation tasks and have obtained promising performance, such as [8, 9, 10].”
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
Chatbots for CRM and Dialogue Management
Galitsky, B. A. (2020) · Human-computer interaction series · doi:10.1007/978-3-030-61641-0_1 · Cited by 6
Neural relation extraction: a review
Aydar, M., Bozal, Ö., & Özbay, F. (2020) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.3906/elk-2005-119 · Cited by 5
Generative Models can Help Writers without Writing for Them.
Arnold, K. C., Volzer, A. M., & Madrid, N. G. (2021) · IUI Workshops · Cited by 5
Enhancing Vietnamese Question Generation with Reinforcement Learning
Nguyen, V., & Nguyen, K. V. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21743-2_45 · Cited by 5
FollowupQG: Towards information-seeking follow-up question generation
Meng, Y., Pan, L., Cao, Y., & Kan, M. (2023) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2023.ijcnlp-main.17 · Cited by 12
Asking the Right Question: Inferring Advice-Seeking Intentions from Personal NarrativesPreprint
Fu, L., Chang, J. P., & Danescu-Niculescu-Mizil, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01587 · Cited by 5
“(2017) for surveys), generate a question that is answerable from the text (question generation (Du et al., 2017)), or answer a question for which the information can be retrieved or reasoned directly from the story (reading comprehension, see Chen (2018) for a survey; notable datasets include MCTest (Richardson, 2013)…”
BackgroundIdentifying Well-formed Natural Language QuestionsPreprint
Faruqui, M., & Das, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.09419 · Cited by 5
“We also demonstrate that such a query well-formedness clas-sifier can be used to improve the quality of a sequence-to-sequence question generation model (Du et al., 2017) by showing an improvement of 0.2 BLEU score in its performance ( § 3).”
BackgroundMethodInfluentialMulti-Task Learning with Language Modeling for Question GenerationPreprint
Zhou, W., Zhang, M., & Wu, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.11813 · Cited by 8
“…QG employ the encoder-decoder framework, which incorporate attention mechanism to pay more attention to the informative part and copy mode to copy some tokens from the input text (Du et al., 2017; Zhou et al., 2017; Song et al., 2018; Subramanian et al., 2018; Zhao et al., 2018; Sun et al., 2018).”
Improving Neural Question Generation using World KnowledgePreprint
Gupta, D. K., Suleman, K., Adada, M., McNamara, A., & Harris, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03716 · Cited by 8
“Du et al. (2017) proposed sequence-to-sequence learning for question generation from text passages.”
BackgroundImproving Question Generation With to the Point ContextPreprint
Li, J., Gao, Y., Bing, L., King, I., & Lyu, M. R.-T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.06036 · Cited by 5
“We evaluate with all commonly-used metrics in question generation (Du et al., 2017): BLEU-”
BackgroundMethodInfluentialSelf-Supervised VQA: Answering Visual Questions using Images and Captions.Preprint
Banerjee, P., Gokhale, T., Yang, Y., & Baral, C. R. (2020) · arXiv (Cornell University) · Cited by 4
Zero-shot Generalization in Dialog State Tracking through Generative Question AnsweringPreprint
Li, S. X., Cao, J. L., Sridhar, M., Zhu, H., Li, S.-W. D., Hamza, W., & McAuley, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.08333 · Cited by 5
“While no such dataset currently exists, in future work we aim to explore using question generation (Du et al., 2017) and paraphrasing (Tseng et al., 2014) models to perform in-domain data augmentation, creating reading comprehension questions for task-oriented dialogs that targeting entities and…”
BackgroundYao, B., Wang, D., Wu, T., Hoang, T. M., Sun, B., Li, T. J.-J., Yu, M., & Xu, Y. (2021) · arXiv (Cornell University) · Cited by 5
Metadata
- Authors
- Du, X., Shao, J., & Cardie, C.
- Affiliations
- Cornell University (US)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Cardie, Claire
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 1342-1352
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2962717047
- Cited by
- 696
- References
- 42
- Citations by year
- 2026: 25 · 2025: 36 · 2024: 54 · 2023: 95 · 2022: 101 · 2021: 120 · 2020: 111 · 2019: 90 · 2018: 56 · 2017: 8
- FWCI
- 33.06
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- automatic question generation, reading comprehension, end-to-end learning, question difficulty
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