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Xiang Gao

Xiang Gao has 3 papers in our library, in 2010–2020, most of them on Language models & pretraining, Dialogue systems; often with Jianfeng Gao, Yizhe Zhang, Baolin Peng.

3 papers in our library · 2010–2020

OpenAlex · ORCID · Wikidata · h-index 17 · 99 works · 2,104 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 678 citations in our library · ⭐ Paper Star rank #2347

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

🎓Beihang UniversityBeijing · China2022–2026 · 30 paperscurrent🎓Huazhong University of Science and TechnologyWuhan · China2026 · 1 papercurrent🏢MicrosoftRedmond · United States2019–2022 · 16 papers🎓Zhejiang UniversityHangzhou · China2019–2021 · 3 papers🎓City University of Hong KongHong Kong · Hong Kong2011 · 3 papers🏢China National Petroleum CorporationBeijing · China2024–2025 · 2 papers🎓Tsinghua UniversityBeijing · China2014–2025 · 2 papers🎓Southeast UniversityNanjing · China2023 · 2 papers🎓Harbin University of Science and TechnologyHarbin · China2011–2012 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 19 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4 Artificial Intelligence · 3 works Arts and Humanities · 3 works Computer Science · 3 works Language and Linguistics · 3 works

🧭 Topics 2 Dialogue systems · 1 work Language models & pretraining · 1 work

 Works by year

2020s

20201 paper

2010s

20191 paper20101 paper

 Papers

The most cited first (OpenAlex and our library).

  1. DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation (2019 · 690 citations)
    Neural chatbots for open-domain conversation often give dull, generic replies, lose track of earlier turns, or shift in style or content.
  2. Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space (2020 · 206 citations)
    Variational Autoencoders (VAEs) can learn compact sentence representations that allow controllable generation.
  3. Exploiting Rich Features for Detecting Hedges and their Scope (2010 · 21 citations)
    This CoNLL-2010 shared task entry tackles two linked problems in biomedical text. The first is finding hedge cues, words or phrases that signal speculation. The second is finding the span of text each cue governs. The authors treat both as sequence labeling with CRFs, building on Morante and Daelemans (2009).

 Co-authors here

12 of 13 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 678 times (of the 141,840 papers whose bibliographies we read); 66 citing papers with a page here →.

Updated

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