6 citations · 6 across the 2 of their papers we have counts for
5 papers · 1 filter
Causal Proxy Models for Concept-Based Model Explanations
Zhengxuan Wu, Karel D'Oosterlinck, Atticus Geiger +2
Explainability methods for NLP systems encounter a version of the fundamental problem of causal inference: for a given ground-truth input text, we never truly observe the counterfa…
Dynabench: Rethinking Benchmarking in NLP
Douwe Kiela, Max Bartolo, Yixin Nie +16
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop datase…
DynaSent: A Dynamic Benchmark for Sentiment Analysis
Christopher Potts, Zhengxuan Wu, Atticus Geiger +1
We introduce DynaSent ('Dynamic Sentiment'), a new English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. DynaSent combines naturally occurring…
Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation
Atticus Geiger, Kyle Richardson, Christopher Potts
We address whether neural models for Natural Language Inference (NLI) can learn the compositional interactions between lexical entailment and negation, using four methods: the beha…
Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences
Atticus Geiger, Ignacio Cases, Lauri Karttunen +1
Standard evaluations of deep learning models for semantics using naturalistic corpora are limited in what they can tell us about the fidelity of the learned representations, becaus…