🧑‍🔬 Lingfei Wu

🏛️ Affiliations

🏢IBMArmonk · United States2018–2022 · 41 papers🏢JingdongBeijing · China2021–2023 · 26 papers🔬IBM Research - Thomas J. Watson Research CenterYorktown Heights · United States2018–2022 · 21 papers🏢PinterestSan Francisco2022–2024 · 12 papers🎓William & MaryWilliamsburg · United States2016–2022 · 12 papers🏛️Chinese Academy of SciencesBeijing · China2009–2023 · 7 papers🎓University of PittsburghPittsburgh · United States2021–2023 · 5 papers🎓University of Science and Technology of ChinaHefei · China2009 · 5 papers🎓Jinan UniversityGuangzhou · China2017–2022 · 4 papers🔬Institute of Intelligent MachinesHefei · China2009 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 22 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.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. A Joint Neural Model for Information Extraction with Global Features (2020 · 602 citations)
   Joint neural information extraction systems of the time still labelled each trigger, entity, relation and argument with its own local classifier.
2. From “Thinking” to “Justifying”: Aligning High-Stakes Explainability with Professional Communication Standards (2026 · 1 citation)
   The paper argues that Chain-of-Thought explanations in high-stakes settings are hard to verify. Because the model reasons before it answers, the reasoning can drift or contain gaps, and the final answer may not match the rationale. The authors borrow from professional writing conventions, namely CREAC from legal…

All works

11 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. A Joint Neural Model for Information Extraction with Global FeaturesAnnual Meeting of the Association for Computational Linguistics (ACL)Ying Lin, Heng Ji, Fei Huang, et al.2020 · 602 citations
2. Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetLuyang Huang, Lingfei Wu, Lu Wang2020 · 203 citations
3. Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases ↗Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · no summary yetYu Chen, Lingfei Wu, Mohammed J. Zaki2019 · 143 citations
4. Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus ↗Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · no summary yetHongyu Gong et al.2019 · 135 citations
5. Word Mover's Embedding: From Word2Vec to Document Embedding ↗Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · no summary yetLingfei Wu et al.2018 · 121 citations
6. SQL-to-Text Generation with Graph-to-Sequence Model ↗Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · no summary yetKun Xu et al.2018 · 99 citations
7. Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem ↗Findings of the Association for Computational Linguistics: EMNLP 2020 · no summary yetShucheng Li et al.2020 · 92 citations
8. Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model ↗Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · no summary yetKun Xu et al.2018 · 87 citations
9. Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning ↗Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yetHanlu Wu et al.2020 · 68 citations
10. From “Thinking” to “Justifying”: Aligning High-Stakes Explainability with Professional Communication StandardsFindings of the Association for Computational LinguisticsChen Qian, Yimeng Wang, Yu Chen, et al.2026 · 1 citation
11. Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks ↗arXiv:2111.10545 · no summary yetHanning Gao et al.2021 · 1 citation

🤝 Co-authors here

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

• Chen Qian
• Fei Huang
• Heng Ji
• Ying Lin
• Yu Chen

Venues

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

• Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing ×4
• Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2020
• Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) ×2
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
• Findings of the Association for Computational Linguistics: EMNLP 2020

🔗 Cited by

Papers of our library cite their works 460 times (of the 149,439 papers whose bibliographies we read); 50 citing papers with a page here →.

Updated 31 October 2026

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