1 citations · 1 across the 3 of their papers we have counts for
5 papers
Few-shot Mining of Naturally Occurring Inputs and Outputs
Mandar Joshi, Terra Blevins, Mike Lewis +2
Creating labeled natural language training data is expensive and requires significant human effort. We mine input output examples from large corpora using a supervised mining funct…
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary
Terra Blevins, Mandar Joshi, Luke Zettlemoyer
Current models for Word Sense Disambiguation (WSD) struggle to disambiguate rare senses, despite reaching human performance on global WSD metrics. This stems from a lack of data fo…
Moving Down the Long Tail of Word Sense Disambiguation with Gloss-Informed Biencoders
Terra Blevins, Luke Zettlemoyer
A major obstacle in Word Sense Disambiguation (WSD) is that word senses are not uniformly distributed, causing existing models to generally perform poorly on senses that are either…
Better Character Language Modeling Through Morphology
Terra Blevins, Luke Zettlemoyer
We incorporate morphological supervision into character language models (CLMs) via multitasking and show that this addition improves bits-per-character (BPC) performance across 24…
Deep RNNs Encode Soft Hierarchical Syntax
Terra Blevins, Omer Levy, Luke Zettlemoyer
We present a set of experiments to demonstrate that deep recurrent neural networks (RNNs) learn internal representations that capture soft hierarchical notions of syntax from highl…