Liling Tan

Natural Language Processing · 2014–2020

A researcher in the Science corpus: 4 papers; trusts 60 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 54 citations into it; #2,441 of 5,688 by trust here, #4,250 by citations
• Artificial Intelligence — 41 citations into it; #13,534 of 35,345 by trust here, #25,040 by citations
• Evaluation & Benchmarks — 30 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it; #14,113 of 21,575 by trust here, #18,782 by citations
• Machine Translation — 5 citations into it; #119 of 292 by trust here, #276 by citations

Whom Liling Tan trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Philipp Koehn
2. Ondrej Bojar
3. Lucia Specia
4. Barry Haddow
5. Matt Post
6. Nadir Durrani
7. Rico Sennrich
8. Chris Callison-Burch
9. Carolina Scarton
10. Franz Joseph Och

Who trusts Liling Tan overall

1. Santanu Pal
2. Shamil Chollampatt
3. Koushik Pahari
4. Sudip Kumar Naskar
5. Weijia Xu
6. Josef van Genabith
7. Raymond Hendy Susanto
8. Alapan Kuila
9. Ondrej Bojar
10. Marine Carpuat

Papers from Liling Tan's perspective

Ordered by how strongly the researchers Liling Tan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Long Short-Term Memory
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
6. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
7. ImageNet classification with deep convolutional neural networks
8. Bleu: a Method for Automatic Evaluation of Machine Translation
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Gradient-based learning applied to document recognition

Their papers

• Lexically Constrained Neural Machine Translation with Levenshtein Transformer (2020) — 87 citations
• Can Automatic Post-Editing Improve NMT? (2020) — 29 citations
• USHEF and USAAR-USHEF participation in the WMT15 QE shared task (2015) — 9 citations
• Manawi: Using Multi-Word Expressions and Named Entities to Improve Machine Translation (2014) — 26 citations