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researcher

Wenhao Yu

Google Inc

40 papers hereh-index 359.1k citations93 works total

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

author position
  • first author8
  • middle author26
  • last author4

Across the 38 of 40 papers where every author was matched, so the position is known.

fields
  • cs.RO23
  • cs.LG8
  • cs.CL4
  • cs.AI2
  • eess.SY2
  • cs.SE1
affiliations
  • Google Inc
Homepage
same name
  • Wenhao Yu — 31 papers, h 18
  • Wenhao Yu — 14 papers, h 9
  • Wenhao Yu — 9 papers, h 3
  • Wenhao Yu — 7 papers, h 3
  • Wenhao Yu — 4 papers, h 1
  • Wenhao Yu — 3 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
20172023
most citedA Survey of Knowledge-Enhanced Text Generation

252 citations · 689 across the 37 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2023★ 19 cited

GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT

Yifan Zhang, Cheng Wei, Shangyou Wu +2

Decision-makers in GIS need to combine a series of spatial algorithms and operations to solve geospatial tasks. For example, in the task of facility siting, the Buffer tool is usua…

cs.CL2023★ 1 cited

Pre-training Language Models for Comparative Reasoning

Mengxia Yu, Zhihan Zhang, Wenhao Yu +1

Comparative reasoning is a process of comparing objects, concepts, or entities to draw conclusions, which constitutes a fundamental cognitive ability. In this paper, we propose a n…

cs.CL2021

Validating Label Consistency in NER Data Annotation

Qingkai Zeng, Mengxia Yu, Wenhao Yu +2

Data annotation plays a crucial role in ensuring your named entity recognition (NER) projects are trained with the right information to learn from. Producing the most accurate labe…

cs.CL2020★ 252 cited

A Survey of Knowledge-Enhanced Text Generation

Wenhao Yu, Chenguang Zhu, Zaitang Li +4

The goal of text generation is to make machines express in human language. It is one of the most important yet challenging tasks in natural language processing (NLP). Since 2014, v…

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