32 citations · 86 across the 16 of their papers we have counts for
8 papers · 1 filter
Effective Feature Representation for Clinical Text Concept Extraction
Yifeng Tao, Bruno Godefroy, Guillaume Genthial +1
Crucial information about the practice of healthcare is recorded only in free-form text, which creates an enormous opportunity for high-impact NLP. However, annotated healthcare da…
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…
An Incremental Iterated Response Model of Pragmatics
Reuben Cohn-Gordon, Noah D. Goodman, Christopher Potts
Recent Iterated Response (IR) models of pragmatics conceptualize language use as a recursive process in which agents reason about each other to increase communicative efficiency. T…
A case for deep learning in semantics
Christopher Potts
Pater's target article builds a persuasive case for establishing stronger ties between theoretical linguistics and connectionism (deep learning). This commentary extends his argume…
Representing Social Media Users for Sarcasm Detection
Y. Alex Kolchinski, Christopher Potts
We explore two methods for representing authors in the context of textual sarcasm detection: a Bayesian approach that directly represents authors' propensities to be sarcastic, and…
Pragmatically Informative Image Captioning with Character-Level Inference
Reuben Cohn-Gordon, Noah Goodman, Christopher Potts
We combine a neural image captioner with a Rational Speech Acts (RSA) model to make a system that is pragmatically informative: its objective is to produce captions that are not me…