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researcher

Rui Wang

44 papers hereh-index 334.6k citations250 works total

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

author position
  • first author5
  • middle author23
  • last author15

Across the 43 of 44 papers where every author was matched, so the position is known.

fields
  • cs.CL15
  • cs.IT13
  • quant-ph4
  • cs.NI3
  • cs.CR2
  • cs.LG2
same name
  • Rui Wang — 34 papers, h 13
  • Rui Wang — 34 papers, h 15
  • Rui Wang — 31 papers, h 26
  • Rui Wang — 27 papers, h 32
  • Rui Wang — 26 papers, h 13
  • Rui Wang — 21 papers, h 22

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
20092025
most citedExperimental Twin-Field Quantum Key Distribution Over 1000 km Fiber Distance

377 citations · 1.1k across the 43 of their papers we have counts for

collaborators
Showing 2019 · cs.CLShow all

4 papers · 2 filters

cs.CL2019

Syntax-Enhanced Self-Attention-Based Semantic Role Labeling

Yue Zhang, Rui Wang, Luo Si

As a fundamental NLP task, semantic role labeling (SRL) aims to discover the semantic roles for each predicate within one sentence. This paper investigates how to incorporate synta…

cs.CL2019★ 1 cited

Revisiting Simple Domain Adaptation Methods in Unsupervised Neural Machine Translation

Haipeng Sun, Rui Wang, Kehai Chen +4

Domain adaptation has been well-studied in supervised neural machine translation (SNMT). However, it has not been well-studied for unsupervised neural machine translation (UNMT), a…

cs.CL2019★ 3 cited

Syntax-aware Neural Semantic Role Labeling

Qingrong Xia, Zhenghua Li, Min Zhang +4

Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP. Motivated by the close correlation between syntactic and semantic…

cs.CL2019★ 2 cited

BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization

Kai Wang, Xiaojun Quan, Rui Wang

The success of neural summarization models stems from the meticulous encodings of source articles. To overcome the impediments of limited and sometimes noisy training data, one pro…

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