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Zhengyuan Liu

Agency for Science, Technology and Research (A*STAR), Singapore

41 papers hereh-index 171.2k citations55 works total

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

author position
  • first author18
  • middle author22

Across the 40 of 41 papers where every author was matched, so the position is known.

fields
  • cs.CL36
  • cs.LG3
  • cs.AI1
  • cs.SD1
affiliations
  • Agency for Science, Technology and Research (A*STAR), Singapore
same name
  • Zhengyuan Liu — 7 papers, h 2
  • Zhengyuan Liu — 5 papers, h 4
  • Zhengyuan Liu — 3 papers, h 2
  • Zhengyuan Liu — 2 papers
  • Zhengyuan Liu — 2 papers, h 7
  • Zhengyuan Liu — 2 papers, h 3

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
20192026
most citedCoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation

43 citations · 83 across the 40 of their papers we have counts for

collaborators
Showing 2020Show all

3 papers · 1 filter

cs.CL2020★ 3 cited

An End-to-End Document-Level Neural Discourse Parser Exploiting Multi-Granularity Representations

Ke Shi, Zhengyuan Liu, Nancy F. Chen

Document-level discourse parsing, in accordance with the Rhetorical Structure Theory (RST), remains notoriously challenging. Challenges include the deep structure of document-level…

cs.CL2020★ 1 cited

Multilingual Neural RST Discourse Parsing

Zhengyuan Liu, Ke Shi, Nancy F. Chen

Text discourse parsing plays an important role in understanding information flow and argumentative structure in natural language. Previous research under the Rhetorical Structure T…

cs.CL2020★ 3 cited

Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization

Zhengyuan Liu, Ke Shi, Nancy F. Chen

Much progress has been made in text summarization, fueled by neural architectures using large-scale training corpora. However, in the news domain, neural models easily overfit by l…

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