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Xiang Kong

Xiang Kong has 2 papers in our library, in 2019–2021, most of them on Machine translation, Low-resource languages; often with Antonios Anastasopoulos, Graham Neubig, Jiatao Gu.

2 papers in our library · 2019–2021

OpenAlex · h-index 12 · 35 works · 798 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 144 citations in our library · 1 more in our library without a page yet · ⭐ Paper Star rank #4109

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

🏢AppleCupertino · United States2024 · 2 paperscurrent🎓Carnegie Mellon UniversityPittsburgh · United States2019–2021 · 13 papers🎓University of Illinois Urbana-ChampaignUrbana · United States2016–2017 · 4 papers🎓Shandong Jiaotong UniversityJinan · China2013–2014 · 3 papers🔬Max Planck Institute for PsycholinguisticsNijmegen · Netherlands2019–2021 · 2 papers🎓Qufu Normal UniversityQufu · China2017 · 2 papers🎓Tsinghua UniversityBeijing · China2013 · 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.

 Concepts, shelves and topics

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

📚 Shelves 4 Artificial Intelligence · 2 works Arts and Humanities · 2 works Computer Science · 2 works Language and Linguistics · 2 works

🧭 Topics 2 Low-resource languages · 1 work Machine translation · 1 work

 Works by year

2020s

20211 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Generalized Data Augmentation for Low-Resource Translation (2019 · 177 citations)
    Neural MT for low-resource languages (LRLs) is hampered by tiny parallel corpora. Ordinary back-translation also helps little here, because the reverse model is itself trained on scarce data. Xia et al. propose a unified augmentation framework that draws on every available resource: English monolingual text, parallel…
  2. Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade (2021 · 141 citations)
    Fully non-autoregressive translation (NAT) models produce every target token in a single parallel forward pass.

All works

3 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. Generalized Data Augmentation for Low-Resource TranslationMengzhou Xia, Xiang Kong, Antonios Anastasopoulos, et al.Annual Meeting of the Association for… · 177 citations
  2. Fully Non-autoregressive Neural Machine Translation: Tricks of the TradeJiatao Gu, Xiang KongFindings of the Association for Computational… · 141 citations
  3. Neural Machine Translation with Adequacy-Oriented Learning (original ↗)Xiang Kong et al.2018 · arXiv:1811.08541 · 45 citations · no summary yet

 Co-authors here

4 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 144 times (of the 141,840 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

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