Elamur Research⌕
☰
home / authors / Laurent Sartran

Laurent Sartran

Laurent Sartran has 2 papers in our library, in 2020–2022, most of them on Machine translation, Language models & pretraining; often with Chris Dyer, Phil Blunsom, Adhiguna Kuncoro.

2 papers in our library · 2020–2022

OpenAlex · ORCID · h-index 6 · 13 works · 133 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 71 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7195

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 Kingdom2020–2025 · 6 paperscurrent🏢GoogleMountain View · United States2020 · 3 papers🎓University of CopenhagenCopenhagen · Denmark2023 · 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.Language models & pretraining · 1 workMachine translation · 1 work

 Works by year

2020s

20221 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. 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…
  2. Better Document-Level Machine Translation with Bayes' Rule (2020 · 72 citations)
    Translating whole documents requires cross-sentence context. For example, Chinese marks tense and number less explicitly than English, so the right English form often depends on other sentences. However, parallel documents for training are scarce. The authors address this by applying Bayes' rule, the classic noisy…

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. 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
  2. Better Document-Level Machine Translation with Bayes' RuleTransactions of the Association for Computational Linguistics (TACL)Lei Yu, Laurent Sartran, Wojciech Stokowiec, et al.2020 · 72 citations
  3. Enabling arbitrary translation objectives with Adaptive Tree Search ↗arXiv:2202.11444 · no summary yetWang Ling et al.2022

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

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/laurent-sartran/