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Joshua Maynez

Joshua Maynez has 4 papers in our library, in 2020–2023, most of them on Question answering, Summarization, Multilingual & cross-lingual NLP; often with Shashi Narayan, Dipanjan Das, Fantine Huot.

4 papers in our library · 2020–2023

OpenAlex · ORCID · h-index 15 · 56 works · 4,608 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 6,699 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #1402

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

🏢GoogleMountain View · United States2018–2026 · 23 paperscurrent🏢Google DeepMindLondon · United Kingdom2021–2024 · 8 papers🎓University of CopenhagenCopenhagen · Denmark2024 · 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.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 work

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

💡 Concepts 1Key ideas and methods explained on our concept pages.Text summarization · 1 work

 Works by year

2020s

20232 papers20211 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. On Faithfulness and Factuality in Abstractive Summarization (2020 · 1,857 citations)
    The paper investigates the extent to which abstractive neural summarization systems hallucinate content that is not present in the input document or that contradicts the facts.
  2. Planning with Learned Entity Prompts for Abstractive Summarization (2021 · 180 citations)
    Neural abstractive summarizers usually generate text end to end, with no explicit intermediate plan. This makes them prone to hallucinations, meaning content that the source does not support. The authors bring back the classic idea of planning before writing. A Transformer encoder-decoder first outputs an entity…
  3. Conditional Generation with a Question-Answering Blueprint (2023 · 70 citations)
    Neural seq-to-seq generators frequently hallucinate, omit important details, and act as black boxes that are hard to inspect or steer.
  4. QAmeleon: Multilingual QA with Only 5 Examples (2023 · 45 citations)
    Extractive question answering datasets are large mainly for English, so QA systems in other languages usually depend on zero-shot transfer or machine-translated training data.

All works

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

  1. On Faithfulness and Factuality in Abstractive SummarizationAnnual Meeting of the Association for Computational Linguistics (ACL)Joshua Maynez, Shashi Narayan, Bernd Bohnet, et al.2020 · 1,857 citations
  2. Planning with Learned Entity Prompts for Abstractive SummarizationTransactions of the Association for Computational Linguistics (TACL)Shashi Narayan, Yao Zhao, Joshua Maynez, et al.2021 · 180 citations
  3. Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings ↗Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetBernd Bohnet et al.2018 · 118 citations
  4. Conditional Generation with a Question-Answering BlueprintTransactions of the Association for Computational Linguistics (TACL)Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo, et al.2023 · 70 citations
  5. QAmeleon: Multilingual QA with Only 5 ExamplesTransactions of the Association for Computational Linguistics (TACL)Priyanka Agrawal, Chris Alberti, Fantine Huot, et al.2023 · 45 citations

 Co-authors here

12 of 16 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 6,699 times (of the 149,439 papers whose bibliographies we read); 1,242 citing papers with a page here →.

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