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Shiyue Zhang

Shiyue Zhang has 2 papers in our library, in 2019–2020, most of them on Question answering; often with Mohit Bansal, Peter Hase.

2 papers in our library · 2019–2020

OpenAlex · ORCID · h-index 18 · 93 works · 1,113 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 139 citations in our library · 2 more in our library without a page yet · ⭐ Paper Star rank #4220

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

🎓Shandong UniversityJinan · China2023–2026 · 5 paperscurrent🎓Beihang UniversityBeijing · China2023–2026 · 2 paperscurrent🎓Zhejiang Gongshang University Hangzhou College of CommerceHangzhou2024–2026 · 2 paperscurrent🎓Yili Normal UniversityYining · China2026 · 1 papercurrent🎓University of North Carolina at Chapel HillChapel Hill · United States2019–2023 · 19 papers🎓Tsinghua UniversityBeijing · China2017–2018 · 8 papers🎓Beijing University of Posts and TelecommunicationsBeijing · China2017–2018 · 7 papers🎓Nanjing University of Information Science and TechnologyNanjing · China2021–2025 · 6 papers🎓Peking UniversityBeijing · China2024–2025 · 4 papers🎓Changzhou UniversityChangzhou · China2023–2024 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 24 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 · 2 works Arts and Humanities · 2 works Computer Science · 2 works Language and Linguistics · 2 works

🧭 Topics 1 Question answering · 1 work

 Works by year

2020s

20201 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering (2019 · 174 citations)
    Neural question generation (QG) models trained only with teacher forcing tend to produce questions whose meaning wanders away from the source passage and target answer.
  2. Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? (2020 · 120 citations)
    Models trained on datasets that pair labels with free-text human explanations can write their own natural language justifications.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Addressing Semantic Drift in Question Generation for Semi-Supervised Question AnsweringShiyue Zhang, Mohit BansalConference on Empirical Methods in Natural… · 174 citations
  2. Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?Peter Hase, Shiyue Zhang, Harry Xie, et al.Findings of the Association for Computational… · 120 citations
  3. ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback (original ↗)Shiyue Zhang, Benjamin Frey, Mohit Bansal2021 · Proceedings of the 59th Annual Meeting of the… · 10 citations · no summary yet
  4. Localizing Factual Inconsistencies in Attributable Text Generation (original ↗)Arie Cattan et al.2026 · Transactions of the Association for… · 6 citations · no summary yet

 Co-authors here

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

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 139 times (of the 141,840 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated

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