Interpretability

Learn to Explain Efficiently via Neural Logic Inductive Learning ICLR 2020 LGML: Logic Guided Machine Learning (Student Abstract) AAAI 2020 LIBRE: Learning Interpretable Boolean Rule Ensembles AISTATS 2020 Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets EMNLP 2020 Linguistic Profiling of a Neural Language Model COLING 2020 Lipschitz-Certifiable Training with a Tight Outer Bound NeurIPS 2020 LOGAN: Local Group Bias Detection by Clustering EMNLP 2020 Logic Constrained Pointer Networks for Interpretable Textual Similarity IJCAI 2020 LS-Tree: Model Interpretation When the Data Are Linguistic AAAI 2020 Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning AAAI 2020 Machine Reasoning: Technology, Dilemma and Future EMNLP 2020 Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations ACL 2020 Mapping natural-language problems to formal-language solutions using structured neural representations ICML 2020 Measuring Alignment to Authoritarian State Media as Framing Bias COLING 2020 Measuring Information Propagation in Literary Social Networks EMNLP 2020 MEMO: A Deep Network for Flexible Combination of Episodic Memories ICLR 2020 MIE: A Medical Information Extractor towards Medical Dialogues ACL 2020 Model-Agnostic Counterfactual Explanations for Consequential Decisions AISTATS 2020 Model Agnostic Multilevel Explanations NeurIPS 2020 Model Class Reliance for Random Forests NeurIPS 2020 Modeling Ambiguity with Many Annotators and Self-Assessments of Annotator Certainty COLING 2020 Modeling Fluency and Faithfulness for Diverse Neural Machine Translation AAAI 2020 Modeling Global Body Configurations in American Sign Language Interspeech 2020 Modeling Noisy Annotations for Crowd Counting NeurIPS 2020 Model Interpretability through the lens of Computational Complexity NeurIPS 2020