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Jiawei Wu

6 papers hereh-index 364.8k citations184 works total

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

author position
  • first author2
  • middle author4

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.CV1
  • cs.LG1
same name
  • Jiawei Wu — 5 papers, h 6
  • Jiawei Wu — 4 papers
  • Jiawei Wu — 3 papers
  • Jiawei Wu — 3 papers, h 4
  • Jiawei Wu — 2 papers, h 2
  • Jiawei Wu — 2 papers

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
20182025
most citedExtract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation

4 citations · 10 across the 3 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2019★ 4 cited

Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation

Jiawei Wu, Xin Wang, William Yang Wang

The overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs. Back-translation has been domina…

cs.CL2019★ 2 cited

Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing

Wenhan Xiong, Jiawei Wu, Deren Lei +4

Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance. Such…

cs.CL2018

Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning

Xin Wang, Jiawei Wu, Da Zhang +2

Although promising results have been achieved in video captioning, existing models are limited to the fixed inventory of activities in the training corpus, and do not generalize to…

cs.CL2018

Reinforced Co-Training

Jiawei Wu, Lei Li, William Yang Wang

Co-training is a popular semi-supervised learning framework to utilize a large amount of unlabeled data in addition to a small labeled set. Co-training methods exploit predicted la…

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