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Question and answer generation is a data augmentation method that aims to improve question answering (QA) models given the limited amount of human labeled data.
A bert baseline for the natural questions
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Good question! statistical ranking for question generation
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Good question! statistical ranking for question generation
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Semi-supervised learning with deep generative models
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Learning to ask: Neural question generation for reading comprehension
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Searchqa: A new q&a dataset augmented with context from a search engine
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Joshi, M., Choi, E., Weld, D. S., and Zettlemoyer, L · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Simple and effective semi-supervised question answering
Dhingra, B., Pruthi, D., and Rajagopal, D · 2018
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Fixing weight decay regularization in adam, 2018
Loshchilov, I. and Hutter, F · 2018
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Know what you don’t know: Unanswerable questions for squad
Rajpurkar, P., Jia, R., and Liang, P · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
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Talmor, A. and Berant, J · 2018
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A simple method for commonsense reasoning
Trinh, T. H. and Le, Q. V · 2018
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Learning to answer by learning to ask: Getting the best of gpt-2 and bert worlds
Klein, T. and Nabi, M · 2019
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Albert: A lite bert for self-supervised learning of language representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R · 2019
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Roberta: A robustly optimized bert pretraining approach
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Zero-shot text classification with generative language models
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Constructing datasets for multi-hop reading comprehension across documents
Welbl, J., Stenetorp, P., and Riedel, S · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W. W., Salakhutdinov, R., and Manning, C. D · 2018
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Dota 2 with large scale deep reinforcement learning
Berner, C., Brockman, G., Chan, B., Cheung, V., Debiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., et al · 2019
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Boolq: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Unified language model pre-training for natural language understanding and generation
Dong, L., Yang, N., Wang, W., Wei, F., Liu, X., Wang, Y., Gao, J., Zhou, M., and Hon, H.-W · 2019
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Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dua, D., Wang, Y., Dasigi, P., Stanovsky, G., Singh, S., and Gardner, M · 2019
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Puri, R. and Catanzaro, B · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Coqa: A conversational question answering challenge
Reddy, S., Chen, D., and Manning, C. D · 2019
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Megatron-lm: Training multi-billion parameter language models using gpu model parallelism
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., and Catanzaro, B · 2019
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MultiQA: An empirical investigation of generalization and transfer in reading comprehension
Talmor, A. and Berant, J · 2019
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Huggingface’s transformers: State-of-the-art natural language processing, 2019
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., and Brew, J · 2019
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Self-training with noisy student improves imagenet classification
Xie, Q., Hovy, E., Luong, M.-T., and Le, Q. V · 2019
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Defending against neural fake news
Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., and Choi, Y · 2019
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Learning to ask unanswerable questions for machine reading comprehension
Zhu, H., Dong, L., Wei, F., Wang, W., Qin, B., and Liu, T · 2019
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