Author: Mingda Chen. Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised Training 3.
finetuning improves claim detection. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 . Ting-Yun Chang and Chi-Jen Lu. 2021. Rethinking why intermediate-task finetuning works. In Findings of the Association for Computational Linguistics: EMNLP 2021. David L. Chen and Raymond J. Mooney. 2008. Learning to sportscast: A test of grounded language acquisition.
Context-aware fine-grained named entity typing. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, and Alexander Rush. 2018. Latent alignment and variational attention. In Advances in Neural Information Processing Systems. Michel Deudon. 2018. Learning semantic similarity in a continuous space. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R.
Conference on Computer Vision and Pattern Recognition.
Improving pre-training by representing and predicting spans. Transactions of the Association for Computational Linguistics, 8:64–77. Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017. TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics . Dan Jurafsky and James H. Martin. 2009.
package for automatic evaluation of summaries. In Text Summarization Branches Out. Dekang Lin and Patrick Pantel. 2001. Discovery of inference rules for questionanswering. Nat. Lang. Eng., 7:343–360. Shuai Lin, Wentao Wang, Zichao Yang, Xiaodan Liang, Frank F. Xu, Eric Xing, and Zhiting Hu. 2020b. Data-to-text generation with style imitation. In Findings of the Association for Computational Linguistics: EMNLP 2020. Xiang Lin, Shafiq Joty, Prathyusha Jwalapuram, and M Saiful Bari. 2019.
large-scale dataset for abstractive and coherent summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, and Lawrence Carin. 2018a. Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics .
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