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Chin-Yew Lin

Microsoft Research Asia

4 papers hereh-index 5434.2k citations179 works total

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

author position
  • middle author2
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL3
  • cs.HC1
affiliations
  • Microsoft Research Asia
Homepage
same name
  • Chin-Yew Lin — 9 papers, h 15
  • Chin-Yew Lin — 9 papers, h 7
  • Chin-Yew Lin — 2 papers, h 2
  • Chin-Yew Lin — 1 paper, h 2

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
20182023
most citedOperations Guided Neural Networks for High Fidelity Data-To-Text Generation

9 citations · 13 across the 4 of their papers we have counts for

collaborators

4 papers

cs.HC2023★ 4 cited

A Survey for Graphic Design Intelligence

Danqing Huang, Jiaqi Guo, Shizhao Sun +6

Graphic design is an effective language for visual communication. Using complex composition of visual elements (e.g., shape, color, font) guided by design principles and aesthetics…

cs.CL2019

Towards Time-Aware Distant Supervision for Relation Extraction

Tianwen Jiang, Sendong Zhao, Jing Liu +5

Distant supervision for relation extraction heavily suffers from the wrong labeling problem. To alleviate this issue in news data with the timestamp, we take a new factor time into…

cs.CL2018★ 9 cited

Operations Guided Neural Networks for High Fidelity Data-To-Text Generation

Feng Nie, Jinpeng Wang, Jin-Ge Yao +2

Recent neural models for data-to-text generation are mostly based on data-driven end-to-end training over encoder-decoder networks. Even though the generated texts are mostly fluen…

cs.CL2018

Incorporating Consistency Verification into Neural Data-to-Document Generation

Feng Nie, Hailin Chen, Jinpeng Wang +3

Recent neural models for data-to-document generation have achieved remarkable progress in producing fluent and informative texts. However, large proportions of generated texts do n…

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