Question Generation With Doubly Adversarial Nets
Abstract
We study the problem of question generation on a specific domain, where there are no labeled data. To address this problem, we propose a novel neural question generation approach called DoubAN, or doubly adversarial nets, which fully utilizes labeled data from other domains (source domains) and unlabeled data from the target domain. Learning a DoubAN involves two adversarial procedures between a question generator and two adversaries. One adversary is a domain-classification discriminator (DC-Dis), which is designed to help the generator learn domain-general representations of the input text. The other is a question-answering discriminator (QA-Dis), which provides more training data with estimated reward scores for generated text-question pairs. We conduct experiments on the SQuAD dataset as target-domain unlabeled data and the NewsQA dataset as source-domain labeled data. Experiment results show that our DoubAN achieves better results than baselines. Compared to model variants, which adopt only DC-Dis or QA-Dis, we find that the DC-Dis and QA-Dis indirectly interact with each other and jointly improve the quality of generated questions on the target domain. Moreover, extensive analysis and discussion prove the reasonableness and effectiveness of our proposed approach.
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The model of [45] gave better results than existing methods.
“Their experimentation proved that their model gave better results than existing methods.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [45]
Reference [45] addresses domain-specific question generation without labeled data using doubly adversarial networks.
“In [45], the authors have addressed the problem of generating questions on a specific domain with the absence of labeled data. For this, they have proposed a new model which uses doubly adversarial networks.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [45]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · 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
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
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
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213 · see citing works
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · 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
Domain-Adversarial Training of Neural Networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318 · see citing works
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) · SSST@EMNLP · doi:10.3115/v1/w14-4012 · Cited by 7,251 · 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
Unsupervised Domain Adaptation by BackpropagationPreprint
Ganin, Y., & Lempitsky, V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.7495 · Cited by 5,360 · see citing works
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322 · see citing works
Domain Adaptation via Transfer Component Analysis
Pan, S. J., Tsang, I. W.-H., Kwok, J. T.-Y., & Yang, Q. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2091281 · Cited by 4,809 · see citing works
Learning Transferable Features with Deep Adaptation Networks
Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015) · arXiv · doi:10.48550/arxiv.1502.02791 · Cited by 4,505 · see citing works
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
Nallapati, R., Zhou, B., dos Santos, C., Gulcehre, C., & Xiang, B. (2016) · Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1028 · Cited by 2,361 · see citing works
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Yu, L., Zhang, W., Wang, Y.-X., & Yu, Y. H. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10804 · Cited by 2,342 · see citing works
Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., & Grauman, K. L. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247911 · Cited by 2,254 · see citing works
Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z., Thorat, N., Viégas, F. B., WATTENBERG, M. P., Corrado, G. S., et al. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00065 · Cited by 1,842 · see citing works
Microsoft COCO Captions: Data Collection and Evaluation Server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollar, P., & Zitnick, C. L. (2015) · arXiv · doi:10.48550/arxiv.1504.00325 · Cited by 1,614 · see citing works
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach
Glorot, X., Bordes, A., & Bengio, Y. (2011) · Cited by 1,569 · see citing works
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
Gu, J., Lu, Z., Li, H., & Li, V. O. K. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1154 · Cited by 1,472 · see citing works
Recurrent Continuous Translation Models
Kalchbrenner, N., & Blunsom, P. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1176 · Cited by 1,277 · see citing works
Convolutional Neural Networks for Sentence ClassificationPreprint
Kim, Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5882 · Cited by 1,112 · see citing works
Domain adaptation for object recognition: An unsupervised approach
Gopalan, R., Li, R., & Chellappa, R. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126344 · Cited by 1,059 · see citing works
Adversarial Learning for Neural Dialogue Generation
Li, J., Monroe, W., Shi, T., Jean, S., Ritter, A., & Jurafsky, D. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1230 · Cited by 880 · see citing works
Transfer Learning for Low-Resource Neural Machine Translation
Zoph, B., Yüret, D., May, J., & Knight, K. K. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1163 · Cited by 802 · see citing works
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
Sordoni, A., Galley, M., Auli, M., Brockett, C., Ji, Y., Mitchell, M., Nie, J., Gao, J., & Dolan, B. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1020 · Cited by 768 · see citing works
Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks
Severyn, A., & Moschitti, A. (2015) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2766462.2767738 · Cited by 736 · see citing works
NewsQA: A Machine Comprehension Dataset
Trischler, A., Wang, T., Yuan, X., Harris, J., Sordoni, A., Bachman, P., & Suleman, K. (2017) · Proceedings of the 2nd Workshop on Representation Learning for NLP · doi:10.18653/v1/w17-2623 · Cited by 706 · cited 2× · see citing works
“We conduct experiments on two existing datasets, SQuAD [26] and NewsQA [27], which was originally created for the MRC task.”
MethodLearning 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 6× · see citing works
“According to [2], we implement a learning-to-ask (L2Ask) baseline in a neural encoder-decoder framework with attention and copying mechanisms.”
BackgroundMethodInfluentialAddressing the Rare Word Problem in Neural Machine Translation
Luong, M.-T., Sutskever, I., Le, Q. V., Vinyals, O., & Zaremba, W. (2015) · doi:10.3115/v1/p15-1002 · Cited by 626 · see citing works
Gülçehre, 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
A Convolutional Encoder Model for Neural Machine Translation
Gehring, J., Auli, M., Grangier, D., & Dauphin, Y. (2016) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p17-1012 · Cited by 470 · see citing works
Unsupervised Domain Adaptation by Domain Invariant Projection
Baktashmotlagh, M., Harandi, M. T., Lovell, B. C., & Salzmann, M. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.100 · Cited by 456 · see citing works
Good Question! Statistical Ranking for Question Generation
Heilman, M., & Smith, N. A. (2018) · Figshare · doi:10.1184/r1/6473390 · Cited by 366 · see citing works
Neural Question Generation from Text: A Preliminary Study
Zhou, Q. S., Yang, N., Wei, F., Tan, C., Bao, H., & Zhou, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-73618-1_56 · Cited by 353 · see citing works
Self-citationBuilding a Semantic Parser Overnight
Wang, Y., Berant, J., & Liang, P. (2015) · doi:10.3115/v1/p15-1129 · Cited by 330 · see citing works
Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification
Chen, X., Sun, Y., Athiwaratkun, B., Cardie, C., & Weinberger, K. Q. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00039 · Cited by 293 · see citing works
Question Generation for Question Answering
Duan, N., Tang, D., Chen, P., & Zhou, M. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1090 · Cited by 271 · see citing works
Self-citationGenerating Natural Questions About an Image
Mostafazadeh, N., Misra, I., Devlin, J., Mitchell, M., He, X., & Vanderwende, L. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1170 · Cited by 270 · see citing works
Reranking and self-training for parser adaptation
McClosky, D., Charniak, E., & Johnson, M. S. (2006) · doi:10.3115/1220175.1220218 · Cited by 237 · see citing works
Generating images with recurrent adversarial networksPreprint
Im, D. J., Kim, C. D., Jiang, H., & Memisevic, R. (2016) · arXiv.org · doi:10.48550/arxiv.1602.05110 · Cited by 232 · see citing works
Neural Generative Question Answering
Yin, J., Jiang, X., Lu, Z., Shang, L., Li, H., & Li, X. (2016) · Proceedings of the Workshop on Human-Computer Question Answering · doi:10.18653/v1/w16-0106 · Cited by 186 · see citing works
Question Answering and Question Generation as Dual TasksPreprint
Tang, D., Duan, N., Qin, T., Yan, Z., & Zhou, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02027 · Cited by 155 · see citing works
Self-citationSemi-Supervised QA with Generative Domain-Adaptive Nets
Yang, Z., Hu, J., Salakhutdinov, R., & Cohen, W. W. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1096 · Cited by 142 · see citing works
Deep Questions without Deep Understanding
Labutov, I., Basu, S., & Vanderwende, L. (2015) · doi:10.3115/v1/p15-1086 · Cited by 141 · see citing works
Generating Natural Language Questions to Support Learning On-Line
Lindberg, D., Popowich, F. P., Nesbit, J. C., & Winne, P. H. (2013) · Cited by 122 · see citing works
Encoding Source Language with Convolutional Neural Network for Machine Translation
Meng, F.-D., Lu, Z.-D., Wang, M., Li, H., Jiang, W.-B., & Liu, Q. (2015) · Annual Meeting of the Association for Computational Linguistics · doi:10.3115/v1/p15-1003 · Cited by 110 · see citing works
Automatic Gap-fill Question Generation from Text Books
Agarwal, M., & Mannem, P. (2011) · Cited by 99 · 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
Aspect-augmented Adversarial Networks for Domain Adaptation
Zhang, Y., Barzilay, R., & Jaakkola, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00077 · Cited by 92 · see citing works
A Joint Model for Question Answering and Question GenerationPreprint
Wang, T., Yuan, X., & Trischler, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.01450 · Cited by 89 · see citing works
Part-of-Speech Tagging for Twitter with Adversarial Neural Networks
Gui, T., Zhang, Q., Huang, H., Peng, M., & Huang, X. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1256 · Cited by 89 · see citing works
Adversarial Deep Averaging Networks for Cross-Lingual Sentiment ClassificationPreprint
Chen, X., Sun, Y., Athiwaratkun, B., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.01614 · Cited by 75 · see citing works
Table-to-Text: Describing Table Region With Natural Language
Bao, J., Tang, D., Duan, N., Yan, Z., Lv, Y., Zhou, M., & Zhao, T. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11944 · Cited by 70 · see citing works
Self-citationTwo-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
Golub, D., Huang, P.-S., He, X., & Deng, L. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1087 · Cited by 53 · see citing works
An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine TranslationPreprint
Chu, C., Dabre, R., & Kurohashi, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.03214 · Cited by 41 · see citing works
Table-to-Text: Describing Table Region with Natural LanguagePreprint
Bao, J., Tang, D., Duan, N., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.11234 · Cited by 14 · see citing works
Self-citationRanking Automatically Generated Questions Using Common Human Queries
Chali, Y., & Golestanirad, S. (2016) · International Natural Language Generation Conference (INLG) · doi:10.18653/v1/w16-6635 · Cited by 11 · see citing works
Flagged first, then by citations
1 of the 64 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Mulla, N., & Gharpure, P. (2023) · Progress in Artificial Intelligence · doi:10.1007/s13748-023-00295-9 · Cited by 132 · see citing works
“In [45], the authors have addressed the problem of generating questions on a specific domain with the absence of labeled data.”
BackgroundA 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
“In addition, some popular frameworks, such as pre-trained framework [53], variational autoencoder [228], graph-based framework [36], and adversarial network [16], have also gained much attention for question generation.”
BackgroundDiverse Distractor Generation for Constructing High-Quality Multiple Choice Questions
Xie, J., Peng, N., Cai, Y., Wang, T., & Huang, Q. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3138706 · Cited by 25 · see citing works
Neural Stance Detection With Hierarchical Linguistic Representations
Wang, Z., Sun, Q., Li, S., Zhu, Q., & Zhou, G. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.2963954 · Cited by 21 · see citing works
“machine translation [38], [39], dependency parsing [40] and question answering system [41].”
BackgroundInterview Bot with Automatic Question Generation and Answer Evaluation
Pandey, R., Chaudhari, D., Bhawani, S., Pawar, O., & Barve, S. (2023) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs57279.2023.10112918 · Cited by 16 · see citing works
“This also consisted of an encoder-decoder framework similar to [10] where input text is first encoded as a vector and then decoded to produce a question [9].”
BackgroundMethodScientific Literature Summarization Using Document Structure and Hierarchical Attention Model
Xu, H., Wang, Z., & Weng, X. (2019) · IEEE Access · doi:10.1109/access.2019.2960611 · Cited by 12 · see citing works
“B. DOCUMENT FEATURES REPRESENTATION As a specific recurrent neural network, long short-term memory (LSTM) is proposed by Hochreiter and Schmidhuber [21], and it has been promoted [22] and widely applied in action recognition [23], [24], image cation [25], [26], and Natural Language Processing [27]–[35].”
BackgroundMulla, N., & Gharpure, P. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01262-2 · Cited by 10 · see citing works
“Several other approaches making use of generative adversarial networks (GANs) have also been attempted [26–28].”
BackgroundInfluentialDevelopment 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
“Question generation is gaining much attention, with popular frameworks like pre-trained framework [26], variational autoencoder [27], graph-based framework [28], and adversarial network [29] becoming increasingly popular.”
BackgroundYu, 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 · see citing works
“One is reinforcement learning which views the answer as a reward (Bao et al., 2018).”
BackgroundMethodDiversified 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 · see citing works
Enhancing Paraphrase Question Generation With Prior Knowledge
Xie, J., Fang, W., Huang, Q., Cai, Y., & Wang, T. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3221045 · Cited by 4 · see citing works
“The BLEU, ROUGE, and METEOR are standard evaluation metrics for the generation task, such as the question generation [10] and paraphrases generation [4].”
BackgroundQuestion Generation Based Product Information
Xiao, K., Zhou, X., Wang, Z., Duan, X., & Zhang, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32236-6_40 · Cited by 1 · see citing works
(2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2018.2867321
Modern Approaches for Automatic Question Paper Generator
Ramar, G., Ramkumar, S., Stephen, R., Balan, R. V. S., & Sahu, M. (2025) · International Conference on Computing Technologies & Data Communication (ICCTDC) · doi:10.1109/icctdc64446.2025.11158798
“Furthermore, this study talks about the challenges in domain differences and evaluation metrics and pointed out that using BLEU scores alone may not always be enough to capture the grammaticality and the relevance of the questions being generated [18].”
- Authors
- Bao, J., Gong, Y., Duan, N., Zhou, M., & Zhao, T.
- Affiliations
- Harbin Institute of Technology (CN)
- Microsoft Research Asia (China) (CN)
- Venue
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Published
- 2018-11
- Volume
- 26
- Issue
- 11
- Pages
- 2230-2239
- ISSN
- 2329-9290 (ISSN-L)
- 2329-9304
- OpenAlex
- W2884022884
- Cited by
- 14
- References
- 64
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 4 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 2 · 2018: 1
- FWCI
- 0.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE/ACM Transactions on Audio Speech and Language Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- question generation, adversarial training, domain adaptation, unlabeled target domain data, reward estimation, SQuAD, cross-domain transfer
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