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Yingce Xia

Yingce Xia has 3 papers in our library, in 2019, most of them on Machine translation, Multilingual & cross-lingual NLP; often with Tao Qin, Tie-Yan Liu, Lijun Wu.

3 papers in our library · 2019

OpenAlex · ORCID · Wikidata · h-index 32 · 112 works · 4,945 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 446 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #3581

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

🎓Peking UniversityBeijing · China2016–2026 · 4 paperscurrent🏢Microsoft Research AsiaBeijing · China2019–2024 · 39 papers🏢MicrosoftRedmond · United States2018–2024 · 24 papers🎓University of Science and Technology of ChinaHefei · China2014–2021 · 18 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 2Research subjects and languages the work is about.Machine translation · 3 worksMultilingual & cross-lingual NLP · 1 work

 Works by year

2010s

20193 papersYingce Xia's papers of 2019

 Papers

The most cited first (OpenAlex and our library).

  1. Multilingual Neural Machine Translation with Language Clustering (2019 · 172 citations)
    Supporting thousands of languages is hard with either extreme: one translation model per language pair is too costly, and a single shared model struggles when its languages are very dissimilar.
  2. Soft Contextual Data Augmentation for Neural Machine Translation (2019 · 172 citations)
    Data augmentation is routine in computer vision but uncommon in neural machine translation. Simple text perturbations such as swapping, dropping or blanking words often distort meaning. Contextual replacement with language-model samples keeps meaning better, but it covers only a small part of the possible variations…
  3. Exploiting Monolingual Data at Scale for Neural Machine Translation (2019 · 102 citations)
    Back translation (BT) is a standard way to use target-language monolingual text in neural machine translation.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Multilingual Neural Machine Translation with Language ClusteringXu Tan, Jiale Chen, Di He, et al.Conference on Empirical Methods in Natural Language Processing (EMNLP) · 172 citations
  2. Soft Contextual Data Augmentation for Neural Machine TranslationFei Gao, Jinhua Zhu, Lijun Wu, et al.Annual Meeting of the Association for Computational Linguistics (ACL) · 172 citations
  3. Exploiting Monolingual Data at Scale for Neural Machine TranslationLijun Wu, Yiren Wang, Yingce Xia, et al.Conference on Empirical Methods in Natural Language Processing (EMNLP) · 102 citations
  4. CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors · original ↗ · no summary yet · Findings of the Association for Computational Linguistics: ACL 2026Hang Su et al.2026

 Co-authors here

8 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 446 times (of the 141,840 papers whose bibliographies we read); 51 citing papers with a page here →.

Updated

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