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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
The remarkable success of pretrained language models has motivated the s...
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Customizing Triggers with Concealed Data Poisoning
Adversarial attacks alter NLP model predictions by perturbing test-time ...
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MedICaT: A Dataset of Medical Images, Captions, and Textual References
Understanding the relationship between figures and text is key to scient...
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Gradient-based Analysis of NLP Models is Manipulable
Gradient-based analysis methods, such as saliency map visualizations and...
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MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics
Posing reading comprehension as a generation problem provides a great de...
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How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations
As local explanations of black box models are increasingly being employe...
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Image Augmentations for GAN Training
Data augmentations have been widely studied to improve the accuracy and ...
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Beyond Accuracy: Behavioral Testing of NLP models with CheckList
Although measuring held-out accuracy has been the primary approach to ev...
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Obtaining Faithful Interpretations from Compositional Neural Networks
Neural module networks (NMNs) are a popular approach for modeling compos...
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Evaluating NLP Models via Contrast Sets
Standard test sets for supervised learning evaluate in-distribution gene...
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Improved Consistency Regularization for GANs
Recent work has increased the performance of Generative Adversarial Netw...
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ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension
Reading comprehension is one of the crucial tasks for furthering researc...
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Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency
As deep reinforcement learning (RL) is applied to more tasks, there is a...
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Neural Module Networks for Reasoning over Text
Answering compositional questions that require multiple steps of reasoni...
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How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods
As machine learning black boxes are increasingly being deployed in domai...
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Memory Augmented Recursive Neural Networks
Recursive neural networks have shown an impressive performance for model...
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Improving Differentially Private Models with Active Learning
Broad adoption of machine learning techniques has increased privacy conc...
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AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Neural NLP models are increasingly accurate but are imperfect and opaque...
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Do NLP Models Know Numbers? Probing Numeracy in Embeddings
The ability to understand and work with numbers (numeracy) is critical f...
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Knowledge Enhanced Contextual Word Representations
Contextual word representations, typically trained on unstructured, unla...
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Universal Adversarial Triggers for Attacking and Analyzing NLP
Adversarial examples highlight model vulnerabilities and are useful for ...
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Universal Adversarial Triggers for NLP
Adversarial examples highlight model vulnerabilities and are useful for ...
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Compositional Questions Do Not Necessitate Multi-hop Reasoning
Multi-hop reading comprehension (RC) questions are challenging because t...
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Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications
Representing entities and relations in an embedding space is a well-stud...
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PoMo: Generating Entity-Specific Post-Modifiers in Context
We introduce entity post-modifier generation as an instance of a collabo...
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DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Reading comprehension has recently seen rapid progress, with systems mat...
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Embedding Multimodal Relational Data for Knowledge Base Completion
Representing entities and relations in an embedding space is a well-stud...
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Interpretation of Natural Language Rules in Conversational Machine Reading
Most work in machine reading focuses on question answering problems wher...
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Compact Factorization of Matrices Using Generalized Round-Rank
Matrix factorization is a well-studied task in machine learning for comp...
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Combining Symbolic and Function Evaluation Expressions In Neural Programs
Neural programming involves training neural networks to learn programs f...
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Multimodal Attribute Extraction
The broad goal of information extraction is to derive structured informa...
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Generating Natural Adversarial Examples
Due to their complex nature, it is hard to characterize the ways in whic...
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Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks
Many real world systems need to operate on heterogeneous information net...
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Programs as Black-Box Explanations
Recent work in model-agnostic explanations of black-box machine learning...
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Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance
At the core of interpretable machine learning is the question of whether...
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Model-Agnostic Interpretability of Machine Learning
Understanding why machine learning models behave the way they do empower...
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Despite widespread adoption, machine learning models remain mostly black...
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Anytime Belief Propagation Using Sparse Domains
Belief Propagation has been widely used for marginal inference, however ...
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Compiling Relational Database Schemata into Probabilistic Graphical Models
Instead of requiring a domain expert to specify the probabilistic depend...
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Distantly Labeling Data for Large Scale Cross-Document Coreference
Cross-document coreference, the problem of resolving entity mentions acr...
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