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researcher

Yao Fu

University of Edinburgh

17 papers hereh-index 164.2k citations18 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author9
  • middle author5
  • last author1

Across the 16 of 17 papers where every author was matched, so the position is known.

fields
  • cs.CL15
  • cs.LG2
affiliations
  • University of Edinburgh
Homepage
same name
  • Yao Fu — 18 papers, h 11
  • Yao Fu — 13 papers, h 50
  • Yao Fu — 10 papers, h 7
  • Yao Fu — 7 papers, h 4
  • Yao Fu — 7 papers, h 4
  • Yao Fu — 6 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192024
most citedDecomposed Prompting: A Modular Approach for Solving Complex Tasks

100 citations · 487 across the 17 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.LG2021★ 1 cited

Scaling Structured Inference with Randomization

Yao Fu, John P. Cunningham, Mirella Lapata

Deep discrete structured models have seen considerable progress recently, but traditional inference using dynamic programming (DP) typically works with a small number of states (le…

cs.CL2021★ 1 cited

Noisy-Labeled NER with Confidence Estimation

Kun Liu, Yao Fu, Chuanqi Tan +4

Recent studies in deep learning have shown significant progress in named entity recognition (NER). Most existing works assume clean data annotation, yet a fundamental challenge in…

cs.CL2021★ 19 cited

Probing BERT in Hyperbolic Spaces

Boli Chen, Yao Fu, Guangwei Xu +4

Recently, a variety of probing tasks are proposed to discover linguistic properties learned in contextualized word embeddings. Many of these works implicitly assume these embedding…

cs.CL2021★ 38 cited

Prototypical Representation Learning for Relation Extraction

Ning Ding, Xiaobin Wang, Yao Fu +7

Recognizing relations between entities is a pivotal task of relational learning. Learning relation representations from distantly-labeled datasets is difficult because of the abund…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.