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Stephan Gouws

Stephan Gouws has 2 papers in our library, in 2017–2018, most of them on Machine translation; often with Anna Goldie, Ashish Vaswani, Brian Strope.

2 papers in our library · 2017–2018

OpenAlex · h-index 12 · 21 works · 6,520 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 229 times in our library · ⭐ Paper Stars ranking: #3656

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

🏢GoogleLondon · United Kingdom2017–2020 · 3 paperscurrent🏢GoogleMountain View · United States2014–2018 · 4 papers🎓Stellenbosch UniversityStellenbosch · South Africa2010–2015 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Concepts, shelves and topics

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

📚 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 1Research subjects and languages the work is about.Machine translation · 1 work

 Works by year

2010s

20181 paperStephan Gouws's papers of 201820171 paperStephan Gouws's papers of 2017

 Papers

The most cited first (OpenAlex and our library).

  1. Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models (2017 · 242 citations)
    Neural seq2seq conversation models tend to produce short, bland replies. When pushed to be longer, their output often becomes repetitive, incoherent or self-contradictory. Working in a single-turn setting, Shao, Gouws et al. (Google) make small changes to the standard model to get long, varied and sensible responses.…
  2. Tensor2Tensor for Neural Machine Translation (2018 · 180 citations)
    This paper presents Tensor2Tensor (T2T), an open-source TensorFlow library from Google. It aims to make deep learning research, especially neural machine translation, faster to run and easier to reproduce. The authors first trace NMT from recurrent sequence-to-sequence models with attention, through convolutional…

 Co-authors here

12 of 13 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 229 times (of the 141,840 papers whose bibliographies we read); 25 citing papers with a page here →.

Updated

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