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Miloš Stanojević

Miloš Stanojević has 2 papers in our library, in 2016–2022, most of them on Machine translation, Language models & pretraining, Evaluation & benchmarks; often with Adhiguna Kuncoro, Amir Kamran, Chris Dyer.

2 papers in our library · 2016–2022

OpenAlex · ORCID · h-index 13 · 42 works · 657 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 216 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #5302

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

🎓Johns Hopkins UniversityBaltimore · United States2026 · 1 papercurrent🎓University of AmsterdamAmsterdam · Netherlands2014–2017 · 15 papers🎓University of EdinburghEdinburgh · United Kingdom2018–2022 · 12 papers🏢Google DeepMindLondon · United Kingdom2022–2024 · 5 papers🎓Charles UniversityPrague · Czechia2012–2016 · 3 papers🎓University College LondonLondon · United Kingdom2024 · 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 3Research subjects and languages the work is about.Language models & pretraining · 1 workEvaluation & benchmarks · 1 workMachine translation · 1 work

 Works by year

2020s

20221 paper

2010s

20161 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Results of the WMT16 Metrics Shared Task (2016 · 177 citations)
    The WMT16 Metrics Shared Task measures how well automatic MT evaluation metrics, which compare system outputs to reference translations, agree with human judgments of quality.
  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

6 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. Results of the WMT16 Metrics Shared TaskConference on Machine Translation: Shared Task PapersOndřej Bojar, Yvette Graham, Amir Kamran, et al.2016 · 177 citations
  2. Results of the WMT15 Metrics Shared Task ↗Proceedings of the Tenth Workshop on Statistical Machine Translation · no summary yetMiloš Stanojević et al.2015 · 153 citations
  3. Fitting Sentence Level Translation Evaluation with Many Dense Features ↗Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yetMiloš Stanojević, Khalil Sima'an2014 · 98 citations
  4. 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
  5. Removing Biases from Trainable MT Metrics by Using Self-Training ↗arXiv:1508.02445 · no summary yetMiloš Stanojevic2015 · 2 citations
  6. Alternative Objective Functions for Training MT Evaluation Metrics ↗Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · no summary yetMiloš Stanojević, Khalil Sima'an2017 · 1 citation

 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 216 times (of the 149,439 papers whose bibliographies we read); 36 citing papers with a page here →.

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