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Adhiguna Kuncoro

Adhiguna Kuncoro has 2 papers in our library, in 2018–2022, most of them on Language models & pretraining, Syntax & grammar formalisms; often with Chris Dyer, Phil Blunsom, Dani Yogatama.

2 papers in our library · 2018–2022

OpenAlex · h-index 17 · 42 works · 2,046 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1,544 times in our library · 7 more in our library without a page yet · ⭐ Paper Stars ranking: #5103

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

🏢Google DeepMindLondon · United Kingdom2019–2026 · 15 paperscurrent🎓University of OxfordOxford · United Kingdom2018–2022 · 10 papers🏢GoogleMountain View · United States2016–2021 · 7 papers🎓Carnegie Mellon UniversityPittsburgh · United States2016–2017 · 6 papers🔬Allen Institute for Artificial IntelligenceSeattle · United States2021–2022 · 2 papers

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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Syntax & grammar formalisms · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20221 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better (2018 · 208 citations)
    Linzen et al. (2016) used subject–verb number agreement as a probe of syntactic competence. The hard cases contain intervening "attractor" nouns whose number differs from the subject. On that basis they argued that LSTM language models fail to capture long-range syntactic dependencies. Kuncoro et al. rerun this test…
  2. Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale (2022 · 72 citations)
    Large Transformer LMs perform strongly without any explicit model of hierarchical syntax. It was unclear whether recursive syntactic composition, which helped small LSTM-based RNNGs, still adds value for Transformers at larger scale. The authors introduce Transformer Grammars (TGs), built on Transformer-XL, which…

All works

9 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. Scaling Language Models: Methods, Analysis & Insights from Training Gopher ↗arXiv:2112.11446 · no summary yetJack W. Rae et al.2021 · 928 citations
  2. Recurrent Neural Network Grammars ↗Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yetChris Dyer et al.2016 · 770 citations
  3. LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them BetterAnnual Meeting of the Association for Computational Linguistics (ACL)Adhiguna Kuncoro, Chris Dyer, John Hale, et al.2018 · 208 citations
  4. Finding syntax in human encephalography with beam search ↗Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetJohn Hale et al.2018 · 182 citations
  5. Unsupervised Recurrent Neural Network Grammars ↗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 yetYoon Kim et al.2019 · 168 citations
  6. IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation ↗Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · no summary yetSamuel Cahyawijaya et al.2021 · 150 citations
  7. What Do Recurrent Neural Network Grammars Learn About Syntax? ↗arXiv:1611.05774 · no summary yetAdhiguna Kuncoro et al.2016 · 83 citations
  8. Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at ScaleTransactions of the Association for Computational Linguistics (TACL)Laurent Sartran, Samuel Barrett, Adhiguna Kuncoro, et al.2022 · 72 citations
  9. Distilling an Ensemble of Greedy Dependency Parsers into One MST Parser ↗Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · no summary yetAdhiguna Kuncoro et al.2016 · 57 citations

 Co-authors here

7 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 1,544 times (of the 149,439 papers whose bibliographies we read); 308 citing papers with a page here →.

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