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[1] Chris Callison-Burch,et al. Most "babies" are "little" and most "problems" are "huge": Compositional Entailment in Adjective-Nouns , 2016, ACL.
[2] Lifu Tu,et al. Pay Attention to the Ending:Strong Neural Baselines for the ROC Story Cloze Task , 2017, ACL.
[3] Blockin Blockin,et al. Quick Training of Probabilistic Neural Nets by Importance Sampling , 2003 .
[4] Josef van Genabith,et al. How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse? , 2017, AMTA.
[5] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[6] Timothy Baldwin,et al. Towards Robust and Privacy-preserving Text Representations , 2018, ACL.
[7] Stephen Pulman,et al. Using the Framework , 1996 .
[8] Sheng Zhang,et al. Ordinal Common-sense Inference , 2016, TACL.
[9] Yejin Choi,et al. The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task , 2017, CoNLL.
[10] Chen Zhang,et al. Towards Conversation Entailment: An Empirical Investigation , 2010, EMNLP.
[11] Carlos Guestrin,et al. Semantically Equivalent Adversarial Rules for Debugging NLP models , 2018, ACL.
[12] Christopher Kanan,et al. Answer-Type Prediction for Visual Question Answering , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Lucy Vanderwende,et al. What Syntax Can Contribute in the Entailment Task , 2005, MLCW.
[14] Chen Zhang,et al. What do We Know about Conversation Participants: Experiments on Conversation Entailment , 2009, SIGDIAL Conference.
[15] Martín Abadi,et al. Learning to Protect Communications with Adversarial Neural Cryptography , 2016, ArXiv.
[16] Yonatan Bisk,et al. Natural Language Inference from Multiple Premises , 2017, IJCNLP.
[17] Yonatan Belinkov,et al. On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference , 2019, *SEMEVAL.
[18] Tiejun Zhao,et al. Attention-Fused Deep Matching Network for Natural Language Inference , 2018, IJCAI.
[19] Masatoshi Tsuchiya,et al. Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment , 2018, LREC.
[20] Yonatan Belinkov,et al. Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects , 2019, Proceedings of the Second Workshop on Shortcomings in Vision and Language.
[21] Pan He,et al. Adversarial Examples: Attacks and Defenses for Deep Learning , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[22] Marco Marelli,et al. A SICK cure for the evaluation of compositional distributional semantic models , 2014, LREC.
[23] Zhen-Hua Ling,et al. Enhanced LSTM for Natural Language Inference , 2016, ACL.
[24] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[25] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[26] Yejin Choi,et al. Story Cloze Task: UW NLP System , 2017, LSDSem@EACL.
[27] Peter Clark,et al. SciTaiL: A Textual Entailment Dataset from Science Question Answering , 2018, AAAI.
[28] Chris Callison-Burch,et al. FrameNet+: Fast Paraphrastic Tripling of FrameNet , 2015, ACL.
[29] Vincent Ng,et al. Resolving Complex Cases of Definite Pronouns: The Winograd Schema Challenge , 2012, EMNLP.
[30] Arul Menezes,et al. Effectively Using Syntax for Recognizing False Entailment , 2006, NAACL.
[31] Omer Levy,et al. Annotation Artifacts in Natural Language Inference Data , 2018, NAACL.
[32] Joyce Y. Chai,et al. Natural language interference from textual entailment to conversation entailment , 2010 .
[33] Stefan Lee,et al. Overcoming Language Priors in Visual Question Answering with Adversarial Regularization , 2018, NeurIPS.
[34] Jakob Uszkoreit,et al. A Decomposable Attention Model for Natural Language Inference , 2016, EMNLP.
[35] J Quinonero Candela,et al. Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment , 2006, Lecture Notes in Computer Science.
[36] Christopher Kanan,et al. Visual question answering: Datasets, algorithms, and future challenges , 2016, Comput. Vis. Image Underst..
[37] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[38] Francis Ferraro,et al. Semantic Proto-Roles , 2015, TACL.
[39] Zachary C. Lipton,et al. How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks , 2018, EMNLP.
[40] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[41] Omer Levy,et al. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding , 2018, BlackboxNLP@EMNLP.
[42] Aapo Hyvärinen,et al. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models , 2010, AISTATS.
[43] Samuel R. Bowman,et al. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference , 2017, NAACL.
[44] James Allen,et al. Tackling the Story Ending Biases in The Story Cloze Test , 2018, ACL.
[45] Nathanael Chambers,et al. A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories , 2016, NAACL.
[46] Dejing Dou,et al. On Adversarial Examples for Character-Level Neural Machine Translation , 2018, COLING.
[47] Rui Yan,et al. Natural Language Inference by Tree-Based Convolution and Heuristic Matching , 2015, ACL.
[48] Dhruv Batra,et al. Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[49] Yonatan Belinkov,et al. Synthetic and Natural Noise Both Break Neural Machine Translation , 2017, ICLR.
[50] Maria Leonor Pacheco,et al. of the Association for Computational Linguistics: , 2001 .
[51] Yoav Goldberg,et al. Adversarial Removal of Demographic Attributes from Text Data , 2018, EMNLP.
[52] Phil Blunsom,et al. Reasoning about Entailment with Neural Attention , 2015, ICLR.
[53] Yoav Goldberg,et al. Breaking NLI Systems with Sentences that Require Simple Lexical Inferences , 2018, ACL.
[54] Ankur Taly,et al. Did the Model Understand the Question? , 2018, ACL.
[55] Adam Tauman Kalai,et al. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings , 2016, NIPS.
[56] Rachel Rudinger,et al. Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation , 2018, BlackboxNLP@EMNLP.
[57] Mirella Lapata,et al. Long Short-Term Memory-Networks for Machine Reading , 2016, EMNLP.
[58] Rachel Rudinger,et al. Hypothesis Only Baselines in Natural Language Inference , 2018, *SEMEVAL.
[59] Yonatan Belinkov,et al. Analysis Methods in Neural Language Processing: A Survey , 2018, TACL.
[60] Kevin Duh,et al. Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework , 2017, IJCNLP.
[61] Christopher Potts,et al. A large annotated corpus for learning natural language inference , 2015, EMNLP.
[62] Holger Schwenk,et al. Supervised Learning of Universal Sentence Representations from Natural Language Inference Data , 2017, EMNLP.
[63] Dan Boneh,et al. Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.
[64] Yash Goyal,et al. Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[65] Dongyeop Kang,et al. AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples , 2018, ACL.
[66] Benjamin Van Durme,et al. Sublinear Partition Estimation , 2015, ArXiv.
[67] Stergios Chatzikyriakidis,et al. Neural Network Models for Natural Language Inference Fail to Capture the Semantics of Inference , 2018, ArXiv.
[68] Yash Goyal,et al. Yin and Yang: Balancing and Answering Binary Visual Questions , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[69] Pasquale Minervini,et al. Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge , 2018, CoNLL.
[70] Percy Liang,et al. Adversarial Examples for Evaluating Reading Comprehension Systems , 2017, EMNLP.
[71] Yang Liu,et al. Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention , 2016, ArXiv.