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Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior.

, , , , , and . MRQA@EMNLP, page 53-57. Association for Computational Linguistics, (2019)

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LocalGAN: Modeling Local Distributions for Adversarial Response Generation., , , , , and . J. Mach. Learn. Res., (2021)Generating Questions from Web Community Contents., , , , and . COLING (Demos), page 467-474. Indian Institute of Technology Bombay, (2012)Predicting Users' Negative Feedbacks in Multi-Turn Human-Computer Dialogues., , , , , and . IJCNLP(1), page 713-722. Asian Federation of Natural Language Processing, (2017)Semorph: A Morphology Semantic Enhanced Pre-trained Model for Chinese Spam Text Detection., , , , and . CIKM, page 1003-1013. ACM, (2022)Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior., , , , , and . MRQA@EMNLP, page 53-57. Association for Computational Linguistics, (2019)Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory., , , , and . ACL (1), page 1343-1353. The Association for Computer Linguistics, (2015)Multimodal DBN for Predicting High-Quality Answers in cQA portals., , , , and . ACL (2), page 843-847. The Association for Computer Linguistics, (2013)Guiding Variational Response Generator to Exploit Persona., , , , , , , and . ACL, page 53-65. Association for Computational Linguistics, (2020)Complex Named Entity Recognition via Deep Multi-task Learning from Scratch., , , , and . NLPCC (1), volume 11108 of Lecture Notes in Computer Science, page 221-233. Springer, (2018)Learning to Generate Structured Queries from Natural Language with Indirect Supervision., , , , and . CoRR, (2018)