Yong Yu

Yong Yu has 2 papers in our library, in 2018–2021, most of them on Machine translation, Text generation; often with Weinan Zhang, Hao Zhou, Lei Li.

2 papers in our library · 2018–2021

current affiliationShanghai Jiao Tong University, Shanghai, China

OpenAlex · ORCID · h-index 76 · 532 works · 29 731 citations OpenAlex, 2026-10-14

2 works in our library · 583 citations in our library · 7 more works in our library · ⭐ Paper Star rank #2551

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

Affiliations

Shanghai Jiao Tong University · Shanghai, China — current · 2001–2026 on 442 papers
Yunnan University · Kunming, China — 2008–2025 on 28 papers
Chinese Academy of Sciences · Beijing, China — 2012–2024 on 4 papers
University of Chinese Academy of Sciences · Beijing, China — 2018–2024 on 4 papers
University of California, Santa Barbara · Santa Barbara, United States — 2021 on 2 papers
Institute of Computing Technology · Beijing, China — 2018–2020 on 2 papers
Yunnan Key Laboratory of Software Engineering · Kunming, China — 2010–2019 on 7 papers
Zhejiang University of Technology · Hangzhou, China — 2011–2013 on 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 18 more institutions on single bylines are not listed.

Topics

Machine translation1 Text generation1

Works by year

2020s

20211 paper

2010s

20181 paper

Papers

The most cited first (OpenAlex and our library).

Texygen: A Benchmarking Platform for Text Generation Models (2018 · 486 citations)
Comparing models for open-domain text generation, especially GAN-based ones, is unreliable for three reasons: no single metric captures model quality, many authors don't release code, and gains in output quality often just come from giving up diversity.
Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021 · 180 citations)
Non-autoregressive translation (NAT) models emit all target tokens at once, which makes them fast. However, they assume target words are independent given the source, so they translate noticeably worse than the autoregressive Transformer. Iterative fixes such as Mask-Predict recover quality but need several decoding…

Also in our library

7 more works of theirs are in our library without a page here yet; they will appear as we publish them.

Co-authors here

4 co-authors with a page, the most papers together first.

Cited by

Papers of our library cite their works 583 times (of the 140 884 papers whose bibliographies we read); 26 citing papers with a page here →.

This page in Markdown → https://md.research.elamur.ai/free/en/authors/yong-yu/

🧑‍🔬 Yong Yu