3 papers
cs.CL2020
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…
cs.CL2020
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…
cs.CL2018
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…