Peter Shaw
Peter Shaw has 2 papers in our library, in 2018–2022; often with Ashish Vaswani, Jakob Uszkoreit, Kristina Toutanova.
OpenAlex · ORCID · Wikidata · h-index 39 · 190 works · 8,405 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 467 times in our library · ⭐ Paper Stars ranking: #962
Affiliations
🎓Wenzhou Medical UniversityWenzhou ·
China2024–2026 · 9 papers🎓Yantai UniversityYantai ·
China2026 · 1 paper🏢Merck & Co., Inc., Rahway, NJ, USARahway ·
United States2006–2023 · 32 papers🎓Nanjing University of Information Science and TechnologyNanjing ·
China2020–2022 · 21 papersEmployment since 2022🏢GoogleMountain View ·
United States2018–2022 · 13 papers🏢Bristol-Myers SquibbNew York ·
United States1998–2007 · 12 papers🤝Menzies School of Health ResearchDarwin ·
Australia2020–2022 · 10 papers🎓Massey UniversityPalmerston North ·
New Zealand2018–2019 · 9 papers🎓New York UniversityNew York ·
United States1991–1998 · 8 papers🏢Ferring PharmaceuticalsMiami ·
United States2017–2020 · 7 papers
Concepts, shelves and topics
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work
💡 Concepts 1Key ideas and methods explained on our concept pages.Transformer and attention · 1 work
Works by year
2020s
2010s
Papers
- Self-Attention with Relative Position Representations (2018 · 2,820 citations)
The Transformer uses neither recurrence nor convolution, so it has no built-in sense of token order. The original model compensates by adding sinusoidal encodings of absolute positions to its inputs. Shaw, Uszkoreit and Vaswani instead build order information directly into self-attention. They treat the input as a… - Improving Compositional Generalization with Latent Structure and Data Augmentation (2022 · 76 citations)
Pre-trained sequence-to-sequence models such as T5 cope well with natural language variation. However, they often fail when test inputs combine familiar elements in unseen ways. Specialized models with discrete latent structure handle such compositions better, but their gains have mostly been limited to synthetic…
Co-authors here
Venues
- Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) ×2: NAACL 2022, NAACL 2018
Cited by
Updated
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