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20122026
most citedFooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods

168 citations · 644 across the 36 of their papers we have counts for

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Showing 2019 · cs.CLShow all

8 papers · 2 filters

cs.CL201914 cited

ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension

Dheeru Dua, Ananth Gottumukkala, Alon Talmor +2

Reading comprehension is one of the crucial tasks for furthering research in natural language understanding. A lot of diverse reading comprehension datasets have recently been intr…

cs.CL201951 cited

Neural Module Networks for Reasoning over Text

Nitish Gupta, Kevin Lin, Dan Roth +2

Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations. Neural module ne…

cs.CL201912 cited

Knowledge Enhanced Contextual Word Representations

Matthew E. Peters, Mark Neumann, Robert L. Logan +4

Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember fa…

cs.CL2019

AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

Eric Wallace, Jens Tuyls, Junlin Wang +3

Neural NLP models are increasingly accurate but are imperfect and opaque---they break in counterintuitive ways and leave end users puzzled at their behavior. Model interpretation m…

cs.CL2019

Do NLP Models Know Numbers? Probing Numeracy in Embeddings

Eric Wallace, Yizhong Wang, Sujian Li +2

The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks. Currently, most NLP models treat numbers in text in the same way as other t…

cs.CL2019

Universal Adversarial Triggers for Attacking and Analyzing NLP

Eric Wallace, Shi Feng, Nikhil Kandpal +2

Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens…