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Taifeng Wang

Taifeng Wang has 2 papers in our library, in 2020–2026, most of them on Language models & pretraining; often with Feng Wang, Wei Chu, Weidi Xu.

2 papers in our library · 2020–2026

OpenAlex · ORCID · h-index 21 · 50 works · 11,391 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 131 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #5699

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

🏢Microsoft Research AsiaBeijing · China2007–2018 · 12 papers🏢Ant GroupHangzhou · China2019–2022 · 10 papers🏢MicrosoftRedmond · United States2014–2020 · 5 papers🏢Alibaba GroupHangzhou · China2020 · 2 papers🎓Georgia Institute of TechnologyAtlanta · United States2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20261 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check (2020 · 199 citations)
    Chinese Spelling Check (CSC) means finding and fixing misused characters in Chinese text. Many such errors involve characters that sound or look alike but differ completely in meaning. Earlier systems used confusion sets only to narrow down candidate replacements. They did not model how similar characters relate to…
  2. TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-training (2026)
    Choosing domain proportions for LLM pre-training data is usually done once, either by hand or with proxy models.

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. SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling CheckAnnual Meeting of the Association for Computational Linguistics (ACL)Xingyi Cheng, Weidi Xu, Kunlong Chen, et al.2020 · 199 citations
  2. PairRE: Knowledge Graph Embeddings via Paired Relation Vectors ↗Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yetLinlin Chao et al.2021 · 183 citations
  3. Document-level Event Extraction via Parallel Prediction Networks ↗Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yetHang Yang et al.2021 · 112 citations
  4. Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetXiexiong Lin et al.2020 · 71 citations
  5. TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-trainingAnnual Meeting of the Association for Computational Linguistics (ACL)Yifan Wang, Binbinliu, Fengze Liu, et al.2026

 Co-authors here

7 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 131 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

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