--- title: "Jiang Bian — author profile and papers — Elamur Research" description: "Jiang Bian, Microsoft: 2 papers on Elamur Research (2019–2026), h-index 29 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/jiang-bian/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/jiang-bian/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Jiang Bian # Jiang Bian Jiang Bian has 2 papers in our library, in 2019–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/); often with [Qingsong Wen](https://md.research.elamur.ai/free/en/authors/qingsong-wen/), [Wei Hong Xu](https://md.research.elamur.ai/free/en/authors/wei-hong-xu/), [Yiyuan Yang](https://md.research.elamur.ai/free/en/authors/yiyuan-yang/). 2 papers in our library · 2019–2026 [OpenAlex](https://openalex.org/A5101544241) · [ORCID](https://orcid.org/0000-0002-9472-600X) · [Wikidata](https://www.wikidata.org/wiki/Q130845731) · [**h-index 29**](https://openalex.org/authors/A5101544241) · [129 works](https://openalex.org/works?filter=author.id:A5101544241) · [4,398 citations](https://openalex.org/works?filter=author.id:A5101544241&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/jiang-bian/#papers) · [🔗 Cited 125 times in our library](https://md.research.elamur.ai/free/en/authors/jiang-bian/cited-by/) · [2 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #5300](https://md.research.elamur.ai/free/en/paper-stars/page/53/#star-5300) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Jiang+Bian) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Jiang+Bian) · [dblp](https://dblp.org/search?q=Jiang+Bian) · [ACL Anthology](https://aclanthology.org/search/?q=Jiang+Bian) ## Affiliations [🏢**Microsoft Research Asia** Beijing · China 2013–2026 · 79 papersEmployment*current*](https://md.research.elamur.ai/free/en/companies/microsoft-research-asia/) · [🏢**Microsoft** Redmond · United States 2019–2026 · 17 papers*current*](https://md.research.elamur.ai/free/en/companies/microsoft/) · [🏢**Baidu** Beijing · China 2023–2024 · 2 papers](https://md.research.elamur.ai/free/en/companies/baidu/) · [🏢**Yahoo** Sunnyvale · United States 2011–2012 · 2 papers](https://md.research.elamur.ai/free/en/companies/yahoo/) · [🎓**Georgia Institute of Technology** Atlanta · United States Education · PhD](https://md.research.elamur.ai/free/en/universities/georgia-institute-of-technology/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 6 The field of science this work belongs to (OpenAlex fields).[Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Networks and Communications · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-networks-and-communications/) · [Signal Processing · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/signal-processing/) 🧭 Topics 3 Research subjects and languages the work is about.[Information extraction · 1 work](https://md.research.elamur.ai/free/en/topics/information-extraction/) · [Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/) ## Works by year ### 2020s [**2026** 1 paper](https://md.research.elamur.ai/free/en/authors/jiang-bian/year/2026/) ### 2010s [**2019** 1 paper](https://md.research.elamur.ai/free/en/authors/jiang-bian/year/2019/) ## Papers The most cited first (OpenAlex and our library). 1. **[Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://md.research.elamur.ai/free/en/papers/zheng-2019-doc2edag-end-end-document-level/)** *(2019 · 246 citations)* Most event extraction (EE) systems only find event arguments within a single sentence. In financial documents, however, the arguments of one event are often spread over several sentences, and one document may describe several events. This work targets document-level EE (DEE) on Chinese financial announcements… 2. **[Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback](https://md.research.elamur.ai/free/en/papers/yang-2026-time-ra-time-series-reasoning/)** *(2026 · 3 citations)* Conventional time series anomaly detection (TSAD) usually just labels data as normal or anomalous. It does not say what kind of anomaly occurred or why. The authors propose TIME-RA, which recasts TSAD as a generative reasoning problem. A (multimodal) LLM must detect an anomaly, assign it to a fine-grained category… ## 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. **[Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://md.research.elamur.ai/free/en/papers/zheng-2019-doc2edag-end-end-document-level/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shun Zheng, Wei Cao, Wei Xu, et al.*2019 · 246 citations* 2. **[Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs ↗](https://arxiv.org/abs/2506.14245) arXiv:2506.14245 · no summary yet**Xumeng Wen et al.*2025 · 62 citations* 3. **[Evaluating the robustness and readiness of large frontier models in health AI applications ↗](https://doi.org/10.1038/s41591-026-04501-8) Nature Medicine · no summary yet**裕二 池谷 et al.*2026 · 4 citations* 4. **[Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback](https://md.research.elamur.ai/free/en/papers/yang-2026-time-ra-time-series-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yiyuan Yang, Zichuan Liu, Lei Song, et al.*2026 · 3 citations* ## Co-authors here 3 co-authors with a page, the most papers together first. - [Qingsong Wen](https://md.research.elamur.ai/free/en/authors/qingsong-wen/) - [Wei Hong Xu](https://md.research.elamur.ai/free/en/authors/wei-hong-xu/) - [Yiyuan Yang](https://md.research.elamur.ai/free/en/authors/yiyuan-yang/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/): [EMNLP 2019](https://md.research.elamur.ai/free/en/venues/emnlp/2019/) - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) - Nature Medicine ## Cited by Papers of our library cite their works 125 times (of the 149,439 papers whose bibliographies we read); [13 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/jiang-bian/cited-by/). Updated 31 October 2026