16 citations · 39 across the 6 of their papers we have counts for
9 papers
Gradual Fine-Tuning for Low-Resource Domain Adaptation
Haoran Xu, Seth Ebner, Mahsa Yarmohammadi +3
Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptatio…
LOME: Large Ontology Multilingual Extraction
Patrick Xia, Guanghui Qin, Siddharth Vashishtha +7
We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions…
Natural Language Inference with Mixed Effects
William Gantt, Benjamin Kane, Aaron Steven White
There is growing evidence that the prevalence of disagreement in the raw annotations used to construct natural language inference datasets makes the common practice of aggregating…
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…
A Framework for Decoding Event-Related Potentials from Text
Shaorong Yan, Aaron Steven White
We propose a novel framework for modeling event-related potentials (ERPs) collected during reading that couples pre-trained convolutional decoders with a language model. Using this…