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Semantic parsing is a technique aimed at constructing a structured representation of the meaning of a natural-language question.
Adversarial training can hurt generalization
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Bad characters: Imperceptible nlp attacks
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An assessment of the range and usefulness of lexical diversity measures and the potential of the measure of textual, lexical diversity (MTLD)
Philip M McCarthy. 2005 · 2005
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Unsupervised semantic parsing
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Mining of massive datasets
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Advances in K-means clustering: a data mining thinking
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Adversarial training methods for semi-supervised text classification
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Towards zero-shot frame semantic parsing for domain scaling
Ankur Bapna, Gokhan Tur, Dilek Hakkani-Tur, and Larry Heck. 2017 · 2017
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Deceiving google’s perspective api built for detecting toxic comments
Hossein Hosseini, Sreeram Kannan, Baosen Zhang, and Radha Poovendran. 2017 · 2017
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Learning a neural semantic parser from user feedback
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, and Luke Zettlemoyer. 2017 · 2017
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Generating natural language adversarial examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang. 2018 · 2018
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Synthetic and natural noise both break neural machine translation
Yonatan Belinkov and Yonatan Bisk. 2018 · 2018
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Active learning for deep semantic parsing
Long Duong, Hadi Afshar, Dominique Estival, Glen Pink, Philip R Cohen, and Mark Johnson. 2018 · 2018
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Hotflip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2018 · 2018
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Improving text-to-sql evaluation methodology
Catherine Finegan-Dollak, Jonathan K Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, and Dragomir Radev. 2018 · 2018
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Black-box generation of adversarial text sequences to evade deep learning classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, and Yanjun Qi. 2018 · 2018
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Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
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A survey on semantic parsing
Aishwarya Kamath and Rajarshi Das. 2018 · 2018
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Textbugger: Generating adversarial text against real-world applications
Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, and Ting Wang. 2018 · 2018
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Deep text classification can be fooled
Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, and Wenchang Shi. 2018 · 2018
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On robustness of neural semantic parsers
Shuo Huang, Zhuang Li, Lizhen Qu, and Lei Pan. 2021 · 2021
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Total recall: a customized continual learning method for neural semantic parsers
Zhuang Li, Lizhen Qu, and Gholamreza Haffari. 2021 · 2021
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 2021
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Few-shot self-rationalization with natural language prompts
Ana Marasović, Iz Beltagy, Doug Downey, and Matthew E Peters. 2021 · 2021
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The curious case of adversarially robust models: More data can help, double descend, or hurt generalization
Yifei Min, Lin Chen, and Amin Karbasi. 2021 · 2021
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Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 2018 · 2018
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Semantically equivalent adversarial rules for debugging nlp models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
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Generating natural adversarial examples
Zhengli Zhao, Dheeru Dua, and Sameer Singh. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Text processing like humans do: Visually attacking and shielding nlp systems
Steffen Eger, Gözde Gül Şahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, and Iryna Gurevych. 2019 · 2019
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Improving adversarial robustness via promoting ensemble diversity
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu. 2019 · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
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Constrained language models yield few-shot semantic parsers
Richard Shin, Christopher Lin, Sam Thomson, Charles Chen Jr, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, and Benjamin Van Durme. 2021 · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
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Adversarial glue: A multi-task benchmark for robustness evaluation of language models
Boxin Wang, Chejian Xu, Shuohang Wang, Zhe Gan, Yu Cheng, Jianfeng Gao, Ahmed Hassan Awadallah, and Bo Li. 2021 · 2021
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Improved ood generalization via adversarial training and pretraing
Mingyang Yi, Lu Hou, Jiacheng Sun, Lifeng Shang, Xin Jiang, Qun Liu, and Zhiming Ma. 2021 · 2021
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Compositional semantic parsing with large language models
Andrew Drozdov, Nathanael Schärli, Ekin Akyuürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, and Denny Zhou. 2022 · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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Towards robustness of text-to-sql models against natural and realistic adversarial table perturbation
Xinyu Pi, Bing Wang, Yan Gao, Jiaqi Guo, Zhoujun Li, and Jian-Guang Lou. 2022 · 2022
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Evaluating the text-to-sql capabilities of large language models
Nitarshan Rajkumar, Raymond Li, and Dzmitry Bahdanau. 2022 · 2022
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Tailor: Generating and perturbing text with semantic controls
Alexis Ross, Tongshuang Wu, Hao Peng, Matthew E Peters, and Matt Gardner. 2022 · 2022
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Clip-forge: Towards zero-shot text-to-shape generation
Aditya Sanghi, Hang Chu, Joseph G Lambourne, Ye Wang, Chin-Yi Cheng, Marco Fumero, and Kamal Rahimi Malekshan. 2022 · 2022
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Few-shot semantic parsing with language models trained on code
Richard Shin and Benjamin Van Durme. 2022 · 2022
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Paraphrasing techniques for maritime qa system
Fatemeh Shiri, Terry Yue Zhuo, Zhuang Li, Shirui Pan, Weiqing Wang, Reza Haffari, Yuan-Fang Li, and Van Nguyen. 2022 · 2022
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Mirac Suzgun, Luke Melas-Kyriazi, and Dan Jurafsky. 2022 · 2022
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An empirical study of gpt-3 for few-shot knowledge-based vqa
Zhengyuan Yang, Zhe Gan, Jianfeng Wang, Xiaowei Hu, Yumao Lu, Zicheng Liu, and Lijuan Wang. 2022 · 2022
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Exploring ai ethics of chatgpt: A diagnostic analysis
Terry Yue Zhuo, Yujin Huang, Chunyang Chen, and Zhenchang Xing. 2023 · 2023
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016 · 2030
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