174 citations · 185 across the 4 of their papers we have counts for
12 papers
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
Harsha Nori, Yin Tat Lee, Sheng Zhang +15
Generalist foundation models such as GPT-4 have displayed surprising capabilities in a wide variety of domains and tasks. Yet, there is a prevalent assumption that they cannot matc…
Modular Self-Supervision for Document-Level Relation Extraction
Sheng Zhang, Cliff Wong, Naoto Usuyama +3
Extracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining…
Joint Universal Syntactic and Semantic Parsing
Elias Stengel-Eskin, Kenton Murray, Sheng Zhang +2
While numerous attempts have been made to jointly parse syntax and semantics, high performance in one domain typically comes at the price of performance in the other. This trade-of…
Universal Decompositional Semantic Parsing
Elias Stengel-Eskin, Aaron Steven White, Sheng Zhang +1
We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language utterances into UDS graph…
The Universal Decompositional Semantics Dataset and Decomp Toolkit
Aaron Steven White, Elias Stengel-Eskin, Siddharth Vashishtha +9
We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantic…
Broad-Coverage Semantic Parsing as Transduction
Sheng Zhang, Xutai Ma, Kevin Duh +1
We unify different broad-coverage semantic parsing tasks under a transduction paradigm, and propose an attention-based neural framework that incrementally builds a meaning represen…