Self-Attention Architectures for Answer-Agnostic Neural Question Generation
Open accessAbstract
Neural architectures based on self-attention, such as Transformers, recently attracted interest from the research community, and obtained significant improvements over the state of the art in several tasks. We explore how Transformers can be adapted to the task of Neural Question Generation without constraining the model to focus on a specific answer passage. We study the effect of several strategies to deal with out-of-vocabulary words such as copy mechanisms, placeholders, and contextual word embeddings. We report improvements obtained over the state-of-the-art on the SQuAD dataset according to automated metrics (BLEU, ROUGE), as well as qualitative human assessments of the system outputs.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In [61], the model using ELMO, placeholding, and copying gave better results on SQuAD.
“The models were evaluated on automatic metrics (BLEU, ROUGE) as well as human evaluation for correctness, fluency, soundness, answerability, and relevance and found that the model employing the ELMO+placeholding+copy mechanism gave better results on SQuAD.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [61]
Generated questions lack semantics and relevance to some extent, as reported in most studies through human evaluation.
“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 [61]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
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
BLEU: 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 · see citing works
Sequence 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 · see citing works
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works
(2004) · Cited by 9,718 · 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
A Fast and Accurate Dependency Parser using Neural Networks
Chen, D., & Manning, C. D. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1082 · Cited by 1,810 · see citing works
Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
Chopra, S., Auli, M., & Rush, A. M. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1012 · Cited by 896 · 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 4× · see citing works
“Three professional English speakers were asked to evaluate, the questions generated by: a) Transformer base , b) our best performing model, and c) the state-of-the-art model by Du et al. (2017) 6 .”
BackgroundMethodInfluentialGülçehre, Ç., Ahn, S., Nallapati, R. M., Zhou, B., & Bengio, Y. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1014 · Cited by 487 · 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
Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks
Zhao, Y., Ni, X., Ding, Y., & Ke, Q. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1424 · Cited by 288 · see citing works
SYSTRAN's Pure Neural Machine Translation SystemsPreprint
Crego, J., Kim, J., Klein, G., Rebollo, A., Yang, K., Sénellart, J., Akhanov, E., Brunelle, P., Coquard, A., Deng, Y., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.05540 · Cited by 122 · see citing works
The first question generation shared task evaluation challenge
Rus, V., Brendan Wyse, A., Piwek, P., Lintean, M. C., Stoyanchev, S., & Moldovan, C. (2010) · Open Research Online (The Open University) · Cited by 95 · see citing works
A Unified Query-based Generative Model for Question Generation and Question AnsweringPreprint
Song, L., Wang, Z., & Hamza, W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.01058 · Cited by 50 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Pavel Levin, Nishikant Dhanuka, Talaat Khalil, Fedor Kovalev, and Maxim Khalilov. 2017. Toward a full-scale neural machine translation in production: the booking. com use case. arXiv preprint arXiv:1709.05820 . · arXiv
Cited by
Sorted by citations
Zhang, Z., Xu, Y., Wang, Y., Yao, B., Ritchie, D., Wu, T., Yu, M., Wang, D., & Li, T. J.-J. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517479 · Cited by 174 · see citing works
Mulla, N., & Gharpure, P. (2023) · Progress in Artificial Intelligence · doi:10.1007/s13748-023-00295-9 · Cited by 132 · see citing works
Zhao, Z., Li, Q., Zhang, Z., Cummins, N., Wang, H., Tao, J., & Schuller, B. W. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.03.013 · Cited by 105 · see citing works
A 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
Hybrid Network Feature Extraction for Depression Assessment from Speech
Zhao, Z., Li, Q., Cummins, N., Liu, B., Wang, H., Tao, J., & Schuller, B. W. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2396 · Cited by 61 · see citing works
Multi-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
Self-attention transfer networks for speech emotion recognition
Zhao, Z., Wang, K., Bao, Z., Zhang, Z., Cummins, N., Sun, S., Wang, H., Tao, J., & Schuller, B. W. (2021) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2020.12.002 · Cited by 35 · see citing works
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 46 · see citing works
Mavi, V., Jangra, A., & Jatowt, A. (2024) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000102 · Cited by 79 · see citing works
Neural 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 27 · see citing works
Synthetic 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 30 · see citing works
Self-citationChainCQG: 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 · see citing works
Question 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 · see citing works
Chai, 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
CriTrainer: An Adaptive Training Tool for Critical Paper Reading
Yuan, K., Lin, H., Cao, S., Peng, Z., Guo, Q., & Ma, X. (2023) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3586183.3606816 · Cited by 25 · see citing works
Two-Staged Self-Attention Based Neural Model For Lung Cancer Recognition
Samarin, A., Savelev, A., & Malykh, V. (2020) · Science and Artificial Intelligence Conference (S.A.I.ence) · doi:10.1109/s.a.i.ence50533.2020.9303206 · Cited by 22 · see citing works
RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question
Mohammadshahi, A., Scialom, T., Yazdani, M., Yanki, P., Fan, A., Henderson, J., & Saeidi, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.428 · Cited by 24 · see citing works
Self-citationMulti-Hop Question Generation with Knowledge Graph-Enhanced Language Model
Li, Z., Cao, Z., Li, P., Zhong, Y., & Li, S. (2023) · Applied Sciences · doi:10.3390/app13095765 · Cited by 16 · see citing works
Socratic Question Generation: A Novel Dataset, Models, and Evaluation
Ang, B. H., Gollapalli, S. D., & Ng, S.-K. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.12 · Cited by 10 · see citing works
CopyBERT: A Unified Approach to Question Generation with Self-Attention
Varanasi, S., Amin, S., & Neumann, G. (2020) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2020.nlp4convai-1.3 · Cited by 11 · see citing works
Diversity Enhanced Narrative Question Generation for Storybooks
Yoon, H., & Bak, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.31 · Cited by 10 · see citing works
Development of system for generating questions, answers, distractors using transformers
Barlybayev, A., & Matkarimov, B. T. (2024) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v14i2.pp1851-1863 · Cited by 15 · 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
What 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 · see citing works
Self-citationDifficulty-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 · see citing works
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 · see citing works
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 · see citing works
Wu, X., Jiang, N., & Wu, Y. (2020) · Workshop on Neural Generation and Translation (WNGT) · doi:10.18653/v1/2020.ngt-1.8 · Cited by 9 · see citing works
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 · see citing works
Generation 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 · see citing works
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 · see citing works
cLegal-QA: a Chinese legal question answering with natural language generation methods
Wang, Y., Shen, X., Huang, Z., Niu, L., & Ou, S. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01675-x · Cited by 7 · see citing works
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 · see citing works
Last, M., & Danon, G. (2020) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1382 · Cited by 6 · see citing works
Wang, 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
CONSISTENT: 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 · see citing works
Towards 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 · see citing works
Yao, 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 · see citing works
Ask To The Point: Open-Domain Entity-Centric Question Generation
Liu, Y., Huang, J., & Chang, K. C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.178 · Cited by 4 · see citing works
Multiple cross-attention for video-subtitle moment retrieval
Fu, H., & Wang, H. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.02.016 · Cited by 4 · see citing works
Automated Multiple-Choice Question Creation Using Synonymization and Factual Confirmation
Pranav, M. K., Deepak, G., & Santhanavijayan, A. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-8403-6_24 · Cited by 4 · see citing works
Lamba, D., & Hsu, W. H. (2021) · 2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT) · doi:10.1109/cecit53797.2021.00052 · Cited by 4 · see citing works
Syntax-guided question generation using prompt learning
Hou, Z., Bi, S., Qi, G., Zheng, Y., Ren, Z., & Li, Y. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09421-7 · Cited by 4 · see citing works
Multi-Lingual Question Generation with Language Agnostic Language Model
Wang, B., Yao, T., Chen, W., Xu, J., & Wang, X. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.199 · Cited by 4 · see citing works
Graph convolutional network for difficulty-controllable visual question generation
Chen, F., Xie, J., Cai, Y., Lin, Z., Li, Q., & Wang, T. (2023) · World Wide Web · doi:10.1007/s11280-023-01202-x · Cited by 3 · see citing works
Zhou, X., Wu, O., & Jiang, C. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108485 · Cited by 3 · see citing works
Transformer-Based Multi-Hop Question Generation (Student Abstract)
Emerson, J., & Chali, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i13.26963 · Cited by 3 · see citing works
QGAE: an end-to-end answer-agnostic question generation model for generating question-answer pairs
Li, L., Zhang, L., Zhu, C., & Mao, Z. (2023) · JUSTC · doi:10.52396/justc-2023-0002 · Cited by 3 · see citing works
Efficient Multi-hop Question Generation
Emerson, J., & Chali, Y. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.08.159 · Cited by 3 · see citing works
Conversational Question Generation in Russian
Makhnytkina, O. V., Matveev, A., Svischev, A., Korobova, P., Zubok, D. A., Mamaev, N., & Tchirkovskii, A. (2020) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct49677.2020.9211056 · Cited by 3 · see citing works
Learning Enhancement Using Question-Answer Generation for e-Book Using Contrastive Fine-Tuned T5
Kumar, S., Chauhan, A., & Kumar, C. P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-24094-2_5 · Cited by 3 · see citing works
Automatic Question Generation: A Comparative Analysis of Rule-Based and Neural Network-Based Models
Velmurugan, H., Pandiarajan, N., Ravi, L., Voleti, S., Sheik, R., & Nirmala, S. J. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-1724-8_21 · Cited by 2 · see citing works
Arabic Question Generation Using Transformers
Alajmi, A., Altabaa, H., Abed, S., & Ahmad, I. (2024) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3701559 · Cited by 2 · see citing works
An Answer Driven Model For Paragraph-level Question Generation
Xu, C., & Xu, J. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534455 · Cited by 2 · see citing works
Speaker Diarization and BERT-Based Model for Question Set Generation from Video Lectures
Nittala, S., Agarwal, P. R., Vishnu, R., & Shanbhag, S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-0095-2_42 · Cited by 2 · see citing works
Improved Synthetic Training for Reading ComprehensionPreprint
Chen, Y., Sultan, M. A., & Castelli, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12776 · Cited by 3 · see citing works
Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment
Rabin, R., Djerbetian, A., Engelberg, R., Hackmon, L., Elidan, G., Tsarfaty, R., & Globerson, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.20 · Cited by 1 · see citing works
Answer-Aware Multi-Hop Question Generation Based on Variant Convolutional Networks
Dong, X., Cheng, S., Zhang, X., & Li, F. (2024) · International Conference on Machine Learning and Natural Language Processing (MLNLP) · doi:10.1109/mlnlp63328.2024.10800274 · Cited by 1 · see citing works
Question Generation via Multi-stage Answers Editing Network
Wang, L., Sheng, D., & Zheng, H.-T. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534027 · Cited by 1 · see citing works
Variational Question-Answer Pair Generation for Machine Reading Comprehension.Preprint
Shinoda, K., & Aizawa, A. (2020) · arXiv (Cornell University) · Cited by 1 · see citing works
Wu, X., Jiang, N., & Wu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.11665
Binary-perspective Asymmetrical Twin Gain: a Novel Evaluation Method for Question Generation
Su, Y., Hong, Y., Zhu, H., Xu, M., Fan, Y., & Zhang, M. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892106
Multi-hop Question Generation without Supporting Fact Information
Emerson, J., & Chali, Y. (2023) · Proceedings of the ... International Florida Artificial Intelligence Research Society Conference · doi:10.32473/flairs.36.133320
Low-Resource Language Question Generation Based on Key Sentence and Knowledge GraphPreprint
Sun, Y., Zhuang, Y., Liu, S., & Zhao, X. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4560896
Qadg: Generating question–answer-distractors pairs for real examination
Zhou, H. Y., & Li, L. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10658-5
Deep Multimodal Learning for Joint Textual and Visual Reasoning
Bordes, P. (2020) · theses.fr (ABES) · doi:10.70675/8bd1beefz5760z4f96z8d8cz87e025608c32
Leveraging Label-Attention Networks and POS Tagging for Generating Chinese Cloze Questions
Hou, Y., Xiong, S., & Li, Y. (2026) · Algorithms · doi:10.3390/a19060501
Unlocking deeper understanding: A type-specific contextualized question generation approach
Dey, D., Rudrapal, D., Jamatia, A., & Chakma, K. (2026) · Sadhana · doi:10.1007/s12046-026-03398-w
- Authors
- Scialom, T., Piwowarski, B., & Staiano, J.
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 6027-6032
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2951785908
- Cited by
- 70
- References
- 18
- Citations by year
- 2026: 2 · 2024: 9 · 2023: 16 · 2022: 13 · 2021: 14 · 2020: 16
- FWCI
- 4.11
- 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)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion, cc-by-nc-sa)
- HAL (submittedVersion)
- 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
- self-attention
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 ›
Are you an author, or do you see a problem with this page? Request a review
