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Hongyuan Mei

Hongyuan Mei has 2 papers in our library, in 2016–2023, most of them on Language models & pretraining, Reasoning; often with Chenhao Tan, Fan Zhou, Jun Zhou.

2 papers in our library · 2016–2023

OpenAlex · ORCID · h-index 11 · 68 works · 1,165 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 217 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #4378

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

🎓Harbin Institute of TechnologyHarbin · China2008–2026 · 26 paperscurrent🎓Toyota Technological Institute at ChicagoChicago · United States2016–2026 · 17 paperscurrent🏛️Ministry of Industry and Information TechnologyBeijing · China2020–2026 · 10 paperscurrent🎓Johns Hopkins UniversityBaltimore · United States2016–2021 · 8 papers🎓University of ChicagoChicago · United States2016–2023 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20231 paper

2010s

20161 paper

 Papers

The most cited first (OpenAlex and our library).

  1. What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment (2016 · 353 citations)
    The paper addresses selective generation: turning a database with many redundant event records into a short natural-language text.
  2. Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning (2023 · 44 citations)
    The paper asks whether large language models can help forecast real-world events in time-stamped sequences, such as political interactions between G20 entities, where each event carries text like a news headline.

All works

5 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. What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine AlignmentConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Hongyuan Mei, Mohit Bansal, Matthew R. Walter2016 · 353 citations
  2. Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences ↗arXiv:1506.04089 · no summary yetHongyuan Mei, Mohit Bansal, Matthew R. Walter2015 · 232 citations
  3. Coherent Dialogue with Attention-Based Language Models ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetHongyuan Mei, Mohit Bansal, Matthew R. Walter2017 · 109 citations
  4. Language Models Can Improve Event Prediction by Few-Shot Abductive ReasoningConference on Neural Information Processing Systems (NeurIPS)Xiaoming Shi, Siqiao Xue, Kangrui Wang, et al.2023 · 44 citations
  5. Accurate Vision-based Vehicle Localization using Satellite Imagery ↗arXiv (Cornell University) · no summary yetHang Chu et al.2015 · 9 citations

 Co-authors here

5 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 217 times (of the 149,439 papers whose bibliographies we read); 31 citing papers with a page here →.

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