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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
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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 101–200 of 695
SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning
Mirzaee, R., Faghihi, H. R., Qiang, N., & Kordjamshidi, P. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.364 · Cited by 66
Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling
Gao, Y., Li, P., King, I., & Lyu, M. R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1480 · Cited by 44
“With the rise of data-driven learning approach and sequence to sequence (seq2seq) framework (Sutskever et al., 2014), Du et al. (2017) first formulate QG as a seq2seq problem with attention mechanism.”
BackgroundInfluentialJoint Learning of Question Answering and Question Generation
Sun, Y., Tang, D., Duan, N., Qin, T., Liu, S., Yan, Z., Zhou, M., Lv, Y., Yin, W., Feng, X., et al. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2897773 · Cited by 40
“We consider QG as a generation problem in this work and use sequence-to-sequence learning (Seq2Seq) [3], [4] as the backbone of the QG model.”
MethodKey Phrase Extraction for Generating Educational Question-Answer Pairs
Willis, A., Davis, G. M., Ruan, S., Manoharan, L., Landay, J. A., & Brunskill, E. (2019) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3330430.3333636 · Cited by 41
Leaf: Multiple-Choice Question Generation
Vachev, K., Hardalov, M., Karadzhov, G., Georgiev, G., Koychev, I., & Nakov, P. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-99739-7_41 · Cited by 45
A Semantic Role-based Approach to Open-Domain Automatic Question Generation
Flor, M., & Riordan, B. (2018) · Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w18-0530 · Cited by 39
“We use the sentence-oriented model5 from Du et al. (2017), where only the input sentence is encoded.”
BackgroundMethodInfluentialMulti-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
Answer-driven Deep Question Generation based on Reinforcement Learning
Wang, L., Xu, Z., Lin, Z., Zheng, H.-T., & Shen, Y. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.452 · Cited by 38
“Neural QG models are usually variants of the encoder-decoder framework (Du et al., 2017; Zhou et al., 2017; Sun et al., 2018; Pan et al., 2019; Wang et al., 2019).”
BackgroundMethodAnswer-based Adversarial Training for Generating Clarification QuestionsPreprint
Rao, S., & Daumé, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.02281 · Cited by 76
“Our clarification question generation model builds on the sequence-to-sequence approach that has proven effective for several language generation tasks (Sutskever et al., 2014; Serban et al., 2016; Yin et al., 2016; Du et al., 2017).”
MethodGenerative Language Models for Paragraph-Level Question Generation
Ushio, A., Alva-Manchego, F. E., & Camacho-Collados, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.42 · Cited by 41
GLGE: A New General Language Generation Evaluation Benchmark
Liu, D., Yan, Y., Gong, Y., Qi, W., Zhang, H., Jiao, J., Chen, W., Fu, J., Shou, L., Gong, M., et al. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.36 · Cited by 42
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
“, 2016), our system considerably outperforms state-of-the-art question generation models (Du et al., 2017; Kumar et al., 2018; Song et al., 2018) in automatic and human evaluation.”
BackgroundMethodResultInfluentialA Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators
Fan, Z., Wei, Z., Wang, S., Liu, Y., & Huang, X. (2018) · International Conference on Computational Linguistics · Cited by 36
Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features
Harrison, V., & Walker, M. A. (2018) · International Conference on Natural Language Generation · doi:10.18653/v1/w18-6536 · Cited by 47
“The rows of Table 5 with labels H&S, Yuan, Du, and S&X refer to the models presented in Heilman and Smith (2010a); Yuan et al. (2017); Du et al. (2017), and Sachan and Xing (2018), respectfully.”
BackgroundMethodInfluentialTell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
Liu, D., Gong, Y., Fu, J., Yan, Y., Chen, J., Lv, J., Duan, N., & Zhou, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.467 · Cited by 37
WeaQA: Weak Supervision via Captions for Visual Question Answering
Banerjee, P., Gokhale, T., Yang, Y., & Baral, C. R. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.302 · Cited by 45
“Several studies (Du et al., 2017; Lewis et al., 2019a) have been dedicated to the complex domain of question generation.”
BackgroundA framework for automatic question generation from text using deep reinforcement learning.Preprint
Kumar, V., Ramakrishnan, G., & Li, Y.-F. (2018) · arXiv (Cornell University) · Cited by 36
Automatic story and item generation for reading comprehension assessments with transformers
Bulut, O., & Yildirim‐Erbasli, S. N. (2022) · International Journal of Assessment Tools in Education · doi:10.21449/ijate.1124382 · Cited by 40
“Existing item generation models (e.g., Du et al., 2017) mostly use sentence-level content to generate items because models show significant performance degradation when applied to paragraph-level or long content.”
MethodAnswer-based Adversarial Training for Generating Clarification Questions
Rao, S., & Daumé, H. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1013 · Cited by 40
“Our clarification question generation model builds on the sequence-to-sequence approach that has proven effective for several language generation tasks (Sutskever et al., 2014; Serban et al., 2016; Yin et al., 2016; Du et al., 2017).”
MethodEvaluating Rewards for Question Generation Models
Hosking, T., & Riedel, S. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1237 · Cited by 34
“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).”
BackgroundMethodInfluentialImproving Unsupervised Question Answering via Summarization-Informed Question Generation
Lyu, C., Shang, L., Graham, Y., Foster, J., Jiang, X., & Liu, Q. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.340 · Cited by 37
“usually borrowed from existing QA datasets (Du et al., 2017; Sun et al., 2018; Ma et al., 2020).”
BackgroundMethodInfluentialGenerating Literal and Implied Subquestions to Fact-check Complex Claims
Chen, J., Sriram, A., Choi, E., & Durrett, G. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.229 · Cited by 34
It is AI’s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children’s Story Books
Yao, B., Wang, D., Wu, T., Zhang, Z., Li, T., Yu, M., & Xu, Y. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.54 · Cited by 51
“Neural-based models for question generation tasks (Du et al., 2017; Zhou et al., 2017; Dong et al., 2019; Scialom et al., 2019; Zhao et al., 2022) have been an emerging research theme in recent years.”
BackgroundIdentifying Well-formed Natural Language Questions
Faruqui, M., & Das, D. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1091 · Cited by 33
“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).”
BackgroundMethodInfluentialInquisitive Question Generation for High Level Text Comprehension
Ko, W.-J., Chen, T.-Y., Huang, Y., Durrett, G., & Li, J. J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.530 · Cited by 39
Automatic Skill-Oriented Question Generation and Recommendation for Intelligent Job Interviews
Qin, C., Zhu, H., Shen, D., Sun, Y., Yao, K., Wang, P., & Xiong, H. (2023) · ACM Transactions on Information Systems · doi:10.1145/3604552 · Cited by 35
“Therefore, several sequence-to-sequence approaches have been proposed to solve this problem in an end-to-end manner [19, 20].”
BackgroundMethodStudent-AI Question Cocreation for Enhancing Reading Comprehension
Liu, M., Zhang, J., Nyagoga, L. M., & Liu, L. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3333439 · Cited by 40
Mavi, V., Jangra, A., & Jatowt, A. (2024) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000102 · Cited by 119
Multimodal Differential Network for Visual Question Generation
Patro, B. N., Kumar, S., Kurmi, V. K., & Namboodiri, V. P. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1434 · Cited by 31
“Recently there have been many deep learning based approaches as well for solving the text-based question generation task such as (Du et al., 2017).”
BackgroundTowards End-to-End Multilingual Question Answering
Loginova, E., Varanasi, S., & Neumann, G. (2020) · Information Systems Frontiers · doi:10.1007/s10796-020-09996-1 · Cited by 33
“The recent success of deep learning question generation (Du et al. 2017) is promising for doing so in an end-to-end (semi-) automated fashion, which is essential for languages with scarce resources.”
BackgroundAutomatic question generation based on sentence structure analysis using machine learning approach
Blšták, M., & Rozinajová, V. (2021) · Natural Language Engineering · doi:10.1017/s1351324921000139 · Cited by 38
“We can use text similarity metrics as presented in (Du et al. 2017) or (Divate and Salgaonkar 2017).”
BackgroundMethodInfluentialControllable Open-ended Question Generation with A New Question Type Ontology
Cao, S., & Wang, L. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.502 · Cited by 42
“Compared to the widely studied task of generating factoid questions that inquire about “one bit” of information (Du et al., 2017; Duan et al., 2017; Li et al., 2019), this work is interested in generating open-ended questions that require deep comprehension and long-form answers (Labutov et al.,…”
CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation
Fei, Z., Zhang, Q., Gui, T., Liang, D., Wang, S., Wu, W., & Huang, X. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.475 · Cited by 35
“outputting the question (Du et al., 2017), which proved to work better than the rule-based methods.”
MethodMixQG: Neural Question Generation with Mixed Answer Types
Murakhovs’ka, L., Wu, C.-S., Laban, P., Niu, T., Liu, W., & Xiong, C. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022 · doi:10.18653/v1/2022.findings-naacl.111 · Cited by 31
Review on Neural Question Generation for Education Purposes
Faraby, S. A., Adiwijaya, A., & Romadhony, A. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00374-x · Cited by 44
How to Ask Good Questions? Try to Leverage Paraphrases
Jia, X., Zhou, W., Sun, X., & Wu, Y. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.545 · Cited by 31
“For current mainstream neural network-based methods on QG, most approaches utilize the Seq2Seq model with attention mechanism (Du et al., 2017; Zhou et al., 2017; Zhao et al., 2018b; Zhou et al., 2019a).”
BackgroundMethodInfluentialZero-shot Generalization in Dialog State Tracking through Generative Question Answering
Li, S., Cao, J. L., Sridhar, M., Zhu, H., Li, S.-W. D., Hamza, W., & McAuley, J. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.91 · Cited by 38
Goyal, R., Kumar, P., & Singh, V. P. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15224-0 · Cited by 35
Liang, Y., Wang, J., Zhu, H., Wang, L., Qian, W., & Lan, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.263 · Cited by 46
Learning Semantic Sentence Embeddings using Sequential Pair-wise DiscriminatorPreprint
Patro, B. N., Kurmi, V. K., Kumar, S., & Namboodiri, V. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.00807 · Cited by 39
“…text embeddings in Machine Translation (Sutskever et al., 2014; Cho et al., 2014; Vinyals and Le, 2015; Wu et al., 2016), question generation (Du et al., 2017), dialogue generation (Shang et al., 2015; Li et al., 2016b; Li et al., 2017a), document summarization (Rush et al., 2015), text…”
BackgroundDeep Representation Learning: Fundamentals, Technologies, Applications, and Open Challenges
Payandeh, A., Baghaei, K. T., Fayyazsanavi, P., Ramezani, S. B., Chen, Z., & Rahimi, S. (2023) · IEEE Access · doi:10.1109/access.2023.3335196 · Cited by 49
Iterative GNN-based Decoder for Question Generation
Fei, Z., Zhang, Q., & Zhou, Y. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.201 · Cited by 30
“Recent works adopted the attention-based sequence-to-sequence neural model for QG tasks, taking answer sentence as input and output the question (Du et al., 2017), which proved to be better than rule-based methods.”
MethodAutomatic Question Generation using Relative Pronouns and Adverbs
Khullar, P., Konigari, R., Hase, M., & Shrivastava, M. (2018) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/p18-3022 · Cited by 29
Multi-Task Learning with Language Modeling for Question Generation
Zhou, W., Zhang, M., & Wu, Y. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1337 · Cited by 29
“…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).”
Das, B., Majumder, M., Phadikar, S., & Sekh, A. A. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11222-2 · Cited by 33
“[19] proposed the attention mechanism framework and later use an encoder-decoder [20] for generating questions from a given paragraph.”
MethodTowards Generating Math Word Problems from Equations and Topics
Zhou, Q. S., & Huang, D. (2019) · International Conference on Natural Language Generation · doi:10.18653/v1/w19-8661 · Cited by 32
“We evaluate the performance of our model using three evaluation metrics following recent text generation works (Du et al., 2017; Zhou et al., 2017; Fan et al., 2018): BLEU (Papineni et al., 2002) is a widely used evaluation method in machine translation and text generation.”
BackgroundMethodNeural Question Generation with Answer Pivot
Wang, B., Wang, X., Tao, T., Zhang, Q., & Xu, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6449 · Cited by 28
“Recently, with the development of deep learning and large-scale question answering dataset, motivated by neural machine translation, Du, Shao, and Cardie (2017) proposed a sequence to sequence (seq2seq) architecture combined with attention mechanism, achieving a promising result on MRC dataset…”
BackgroundMethodInfluentialSynthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
Riabi, A., Scialom, T., Keraron, R., Sagot, B., Seddah, D., & Staiano, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.562 · Cited by 44
“On English corpora, generating synthetic questions has shown to significantly improve the performance of QA models (Du et al., 2017; Golub et al., 2017; Du and Cardie, 2018; Alberti et al., 2019).”
BackgroundAutomatic True/False Question Generation for Educational Purpose
Zou, B., Li, P., Pan, L., & Aw, A. T. (2022) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/2022.bea-1.10 · Cited by 31
“Depending on the QA datasets used for training, various types of questions can be generated such as span-based questions (Du et al., 2017; Gao et al., 2019), multiple-choice questions (Chung et al., 2020), and multi-hop questions (Pan et al., 2020; Su et al., 2020).”
BackgroundChainCQG: Flow-Aware Conversational Question Generation
Gu, J., Mirshekari, M., Yu, Z., & Sisto, A. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.177 · Cited by 28
Learning to Answer Visual Questions From Web Videos
Yang, A., Miech, A., Šivic, J., Laptev, I., & Schmid, C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3173208 · Cited by 36
“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.”
CReBot: Exploring interactive question prompts for critical paper reading
Peng, Z., Liu, Y., Zhou, H., Xu, Z., & Ma, X. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102898 · Cited by 27
Transformer-based End-to-End Question Generation.Preprint
Lopez, L. E., Cruz, D. K., Cruz, J. C. B., & Cheng, C. K. (2020) · arXiv (Cornell University) · Cited by 26
Visual Question Generation: The State of the Art
Patil, C., & Patwardhan, M. (2021) · ACM Computing Surveys · doi:10.1145/3383465 · Cited by 25
“Another inverse question answering approach is suggested in Du et al. [12] that uses Bi-LSTM.”
BackgroundOn the Generation of Medical Question-Answer Pairs
Shen, S., Li, Y., Du, N., Wu, X., Xie, Y., Ge, S., Yang, T., Wang, K., Liang, X., & Fan, W. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6410 · Cited by 25
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 30
Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases
Bi, S., Cheng, X., Li, Y.-F., Wang, Y., & Qi, G. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.250 · Cited by 26
Graph Neural Networks for Natural Language Processing: A Survey
Wu, L., Chen, Y., Shen, K., Guo, X., Gao, H., Li, S., Pei, J., & Long, B. (2023) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000096 · Cited by 417
Automatic Question Generation from Children’s Stories for Companion Chatbot
Lee, C.-H., Chen, T.-Y., Chen, L.-P., Yang, P.-C., & Tsai, R. T. (2018) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2018.00078 · Cited by 25
What’s The Latest? A Question-driven News Chatbot
Laban, P., Canny, J., & Hearst, M. A. (2020) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2020.acl-demos.43 · Cited by 27
“A common approach is to use a sequence to sequence model (Du et al., 2017), encoding the paragraph (or context), an optional target answer (answer-aware (Sun et al.”
Contrastive Domain Adaptation for Question Answering using Limited Text Corpora
Yue, Z., Kratzwald, B., & Feuerriegel, S. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.754 · Cited by 29
“Du et al. (2017) propose an endto-end seq2seq encoder-decoder for the generation.”
BackgroundQuestion Generation for Reading Comprehension Assessment by Modeling How and What to Ask
Ghanem, B., Coleman, L. L., Dexter, J. R., von der Ohe, S., & Fyshe, A. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.168 · Cited by 27
Hwang, M.-H., Shin, J., Seo, H., Im, J.-S., Cho, H., & Lee, C.-K. (2023) · Applied Sciences · doi:10.3390/app13020903 · Cited by 28
An Empirical Comparison of LM-based Question and Answer Generation Methods
Ushio, A., Alva-Manchego, F. E., & Camacho-Collados, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.899 · Cited by 30
Artificial Canaries: Early Warning Signs for Anticipatory and Democratic Governance of AI.
Cremer, C. Z., & Whittlestone, J. (2021) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2021.02.011 · Cited by 26
Qi, P., Zhang, Y., & Manning, C. D. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.3 · Cited by 23
Towards Answer-unaware Conversational Question Generation
Nakanishi, M., Kobayashi, T., & Hayashi, Y. (2019) · Proceedings of the 2nd Workshop on Machine Reading for Question Answering · doi:10.18653/v1/d19-5809 · Cited by 21
“Succeeding the pioneering work (Du et al., 2017), several proposals (Zhou et al., 2017; Du and Cardie, 2018; Yuan et al., 2017; Tang et al., 2017) have been made to chiefly improve the quality of generated questions.”
BackgroundMethodA Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation
Narayan, S., Simões, G., Zhao, Y., Maynez, J., Das, D., Collins, M., & Lapata, M. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.94 · Cited by 27
HRVQA: A Visual Question Answering benchmark for high-resolution aerial images
Li, K., George Vosselman, A., & Yang, M. Y. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.06.002 · Cited by 32
Reinforced Multi-task Approach for Multi-hop Question Generation
Gupta, D. K., Chauhan, H., Akella, R. T., Ekbal, A., & Bhattacharyya, P. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.249 · Cited by 25
Contrastive Multi-document Question Generation
Cho, W. S., Zhang, Y., Rao, S., Çelikyılmaz, A., Xiong, C., Gao, J., Wang, M., & Dolan, B. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.2 · Cited by 31
Chai, Z., & Wan, X. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.21 · Cited by 25
“BLEU 1 BLEU 2 BLEU 3 ROUGE METEOR Length Seq2seq (Du et al., 2017) 28.72 10.16 6.30 31.75 13.10 5.78 CopyNet (See et al., 2017) 29.40 12.14 6.53 33.71 14.20 5.77 CoreNQG (Du and Cardie, 2018)”
BackgroundMethodInfluentialAsking It All: Generating Contextualized Questions for any Semantic Role
Pyatkin, V., Roit, P., Michael, J., Goldberg, Y., Tsarfaty, R., & Dagan, I. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.108 · Cited by 28
“…syntactic trees (Heilman and Smith, 2010; Dhole and Manning, 2020) and SRL parses (Mazidi and Nielsen, 2014; Flor and Riordan, 2018), as well as training seq2seq models conditioned on the ques-tion’s answer (FitzGerald et al., 2018) or a text passage containing the answer (Du et al., 2017).”
Reinforcement learning-driven deep question generation with rich semantics
Guan, M., Mondal, S. K., Dai, H., & Bao, H. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103232 · Cited by 28
Zhao, R., Tang, J., Zeng, W., Guo, Y., & Zhao, X. H. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.104044 · Cited by 28
Question Headline Generation for News Articles
Zhang, R., Guo, J., Fan, Y., Lan, Y., Xu, J., Cao, H., & Cheng, X. (2018) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3269206.3271711 · Cited by 23
“Du et al. [14] proposed to directly map a sentence from a text passage to a question using a traditional Seq2Seq model with de-coder attention.”
BackgroundMethodInfluentialOn Training Instance Selection for Few-Shot Neural Text Generation
Chang, E., Shen, X., Yeh, H.-S., & Demberg, V. (2021) · doi:10.18653/v1/2021.acl-short.2 · Cited by 23
Automatic Question Generation for Repeated Testing to Improve Student Learning Outcome
Tsai, D. C. L., Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2021) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt52272.2021.00108 · Cited by 23
Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI
(2020) · doi:10.18653/v1/2020.nlp4convai-1 · Cited by 23
“A QG system can be used in the creation of artificial question answering datasets which in-turn helps QA (Duan et al., 2017).”
Question Answering Infused Pre-training of General-Purpose Contextualized Representations
Jia, R., Lewis, M., & Zettlemoyer, L. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.59 · Cited by 29
Entity Guided Question Generation with Contextual Structure and Sequence Information Capturing
Huang, Q., Fu, M., Mo, L., Cai, Y., Xu, J., Li, P., Li, Q., & Leung, H.-F. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17544 · Cited by 22
“Models GRAM REL ANS Seq2Seq (Du et al. 2017) 3.48 3.28 3.18 S2Sa-at-mp-gsa (Zhao et al. 2018) 3.71 3.77 3.32 Unified-model (Zhou et al. 2019) 3.65 4.29 3.89 G2S-Bert-RL (Chen et al. 2020) 3.86 4.33 3.78 Syn-QG (Dhole et al. 2020) 3.93 4.28 3.85 EGSS (ours) 4.06 4.37 4.01 Table 4: Human evaluation…”
MethodInfluentialNewsPod: Automatic and Interactive News Podcasts
Laban, P., Ye, E., Korlakunta, S., Canny, J., & Hearst, M. A. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511147 · Cited by 26
“In question generation [12, 47], a text generation model is usually given a paragraph and tasked with generating a question that can be answered by the paragraph.”
BackgroundIndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages
Kumar, A., Shrotriya, H., Sahu, P., Mishra, A., Dabre, R., Puduppully, R., Kunchukuttan, A., Khapra, M. M., & Kumar, P. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.360 · Cited by 47
“Question generation is the task of generating a question given some context and the answer (Du et al., 2017; Zhou et al., 2017).”
BackgroundMethodInfluentialZhang, Y., Lin, H., Yang, Z., Wang, J., & Sun, Y. (2019) · Database · doi:10.1093/database/baz054 · Cited by 22
“Recent studies (19, 20) have suggested attention mechanisms can effectively emphasize the relatively important parts of the input sentences and be helpful in boosting the performance of relation extraction.”
BackgroundEmbedding-based Zero-shot Retrieval through Query GenerationPreprint
Liang, D., Xu, P., Shakeri, S., dos Santos, C. N., Nallapati, R. M., Huang, Z., & Xiang, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.10270 · Cited by 39
“Du et al. (2017) first applied the seq2seq model for automatic question generation from text in reading comprehension.”
MethodSelecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation
Yuan, X., Wang, T., Wang, Y.-H., Fine, E., Abdelghani, R., Sauzéon, H., & Oudeyer, P.-Y. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.820 · Cited by 21
Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata
Kaffee, L.-A., Elsahar, H., Vougiouklis, P., Gravier, C., Laforest, F., Hare, J. S., & Simperl, E. P. B. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2101 · Cited by 21
“IR is an Information Retrieval (IR) baseline similar to those that have been used in other text generative tasks (Rush et al., 2015; Du et al., 2017).”
MethodFluent Response Generation for Conversational Question Answering
Baheti, A., Ritter, A., & Small, K. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.19 · Cited by 26
Fine-grained Concept Linking using Neural Networks in Healthcare
Dai, J., Zhang, M., Chen, G., Fan, J., Ngiam, K. Y., & Ooi, B. C. (2018) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3183713.3196907 · Cited by 21
“The encode-decode process has achieved superior performance in many applications, such as machine translation [5] and reading comprehension [10].”
BackgroundSequence-to-Sequence Learning for Indonesian Automatic Question Generator
Muis, F. J., & Purwarianti, A. (2020) · International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA) · doi:10.1109/icaicta49861.2020.9429032 · Cited by 25
“In this work we construct an Indonesian automatic question generator, adapting the architecture from some previous works [1][2].”
BackgroundMethodFFCI: A Framework for Interpretable Automatic Evaluation of Summarization
Koto, F., Baldwin, T., & Lau, J. H. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13167 · Cited by 25
QDG: A unified model for automatic question-distractor pairs generation
Shuai, P., Li, L., Liu, S., & Shen, J. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03894-6 · Cited by 20
“Therefore, the sequence-to-sequence model was introduced to the QG task [7, 8, 42] and achieved auspicious performance.”
MethodEnd-to-End Synthetic Data Generation for Domain Adaptation of Question Answering SystemsPreprint
Shakeri, S., dos Santos, C. N., Zhu, H., Ng, P., Nan, F., Wang, Z., Nallapati, R. M., & Xiang, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06028 · Cited by 20
“Although many past works have proposed different strategies for question generation, they have limited or no success in improving the downstream QA task (Du et al., 2017; Sun et al., 2018; Song et al., 2018; Klein and Nabi, 2019; Wang et al., 2020; Ma et al., 2020; Chen et al., 2020; Tuan et al., 2019).”
BackgroundQuestion Answering Survey: Directions, Challenges, Datasets, Evaluation MatricesPreprint
Pandya, H. A., & Bhatt, B. (2021) · Preprints.org · doi:10.20944/preprints202112.0136.v1 · Cited by 20
Generating fact checking briefs
Fan, A., Piktus, A., Petroni, F., Wenzek, G., Saeidi, M., Vlachos, A., Bordes, A., & Riedel, S. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.580 · Cited by 42
A question–answer generation system for an asynchronous distance learning platform
Wang, H., Maslim, M., & Kan, C.-H. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11675-y · Cited by 22
Uto, M., Tomikawa, Y., & Suzuki, A. (2023) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/2023.bea-1.10 · Cited by 28
“End-to-end QG methods based on deep neural networks have received wide attention as a means of overcoming this limitation (Chan and Fan, 2019; Du et al., 2017; Ushio et al., 2022; Yu et al., 2023; Zhang et al., 2021).”
MethodQuestion Generation for Language Learning: From ensuring texts are read to supporting learning
Chinkina, M., & Meurers, D. (2017) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w17-5038 · Cited by 23
“Finally, QG is not an exception to the wave of neural networks, and Du et al. (2017) have recently approached automatic generation of reading comprehension questions on that basis.”
Background
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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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