Sander Wubben
Natural Language Processing · 2012–2019
A researcher in the Science corpus: 5 papers; trusts 72 researchers and is trusted by 15 researchers here, as inferred from the citation graph.
Subfields
- Natural Language Processing — 67 citations into it; #2,645 of 5,688 by trust here, #3,053 by citations
- Artificial Intelligence — 65 citations into it; #11,018 of 35,345 by trust here, #11,072 by citations
- Evaluation & Benchmarks — 34 citations into it; #1,753 of 3,147 by trust here, #1,728 by citations
- Computer Vision and Pattern Recognition — 9 citations into it
- Computational Linguistics & Semantics — 5 citations into it
Whom Sander Wubben trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Franz Joseph Och
- Andreas Stolcke
- Philipp Koehn
- Dekang Lin
- Kenji Yamada
- David A. Smith
- Kevin Knight
- Ehud Reiter
- Chris Quirk
- Zhemin Zhu
Who trusts Sander Wubben overall
- Wei Xu
- Fernando Alva-Manchego
- Sumit Chopra
- Alexander M. Rush
- Claire Gardent
- Thiago Castro Ferreira
- Sudha Rao
- Elior Sulem
- Nariman Farsad
- Louis Martin
Papers from Sander Wubben's perspective
Ordered by how strongly the researchers Sander Wubben trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Bleu: a Method for Automatic Evaluation of Machine Translation
- Gradient-based learning applied to document recognition
Their papers
- Best practices for the human evaluation of automatically generated text (2019) — 276 citations
- Enriching the WebNLG corpus (2018) — 59 citations
- Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation (2017) — 36 citations
- Towards more variation in text generation: Developing and evaluating variation models for choice of referential form (2016) — 30 citations
- Sentence Simplification by Monolingual Machine Translation (2012) — 319 citations