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Dongling Xiao

Dongling Xiao has 2 papers in our library, in 2020, most of them on Language models & pretraining, Text generation; often with Haifeng Wang, Han Zhang, Hao Tian.

2 papers in our library · 2020

OpenAlex · h-index 6 · 16 works · 262 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 56 times in our library · ⭐ Paper Stars ranking: #5825

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

🏢State Grid Corporation of ChinaBeijing · China2021–2025 · 4 paperscurrent🏢State Grid Hubei Electric Power Co., Ltd.Wuhan · China2021–2025 · 4 paperscurrent🏢BaiduBeijing · China2020–2022 · 4 papers🏢TencentShenzhen · China2023–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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 · 2 worksText generation · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.N-gram · 1 work

 Works by year

2020s

20202 papers

 Papers

The most cited first (OpenAlex and our library).

  1. ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation (2020 · 163 citations)
    ERNIE-GEN, from Baidu, is a sequence-to-sequence framework that covers both pre-training and fine-tuning for text generation tasks.
  2. ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding (2020 · 22 citations)
    BERT-style masked language modeling learns mostly from fine-grained units, such as subwords in English or characters in Chinese. Earlier extensions mask whole spans, but they still predict each token of a span separately. This ignores how tokens inside an n-gram depend on one another and how different n-grams relate.

 Co-authors here

6 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 56 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

Updated

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