Carolina Scarton
Artificial Intelligence · with Specia · 2014–2020
A researcher in the Science corpus: 11 papers; trusts 68 researchers and is trusted by 36 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 191 citations into it; #6,494 of 35,345 by trust here, #3,289 by citations
- Natural Language Processing — 190 citations into it; #1,682 of 5,688 by trust here, #822 by citations
- Evaluation & Benchmarks — 113 citations into it; #1,074 of 3,147 by trust here, #522 by citations
- Machine Translation — 50 citations into it; #40 of 292 by trust here, #168 by citations
- Information Systems — 15 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it
- Statistics and Probability — 7 citations into it
- Representation & Self-Supervised Learning — 6 citations into it
Whom Carolina Scarton trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Lucia Specia
- Matthew G. Snover
- Satanjeev Banerjee
- Kashif Shah
- Bonnie J. Dorr
- Ondrej Bojar
- Tomas Mikolov
- Alon Lavie
- Trevor Cohn
- Eugene Charniak
Who trusts Carolina Scarton overall
- Lucia Specia
- Ondrej Bojar
- David Langlois
- Varvara Logacheva
- Gustavo Paetzold
- Rajen Chatterjee
- Christian Federmann
- Daniel Beck
- Liling Tan
- Kashif Shah
Papers from Carolina Scarton's perspective
Ordered by how strongly the researchers Carolina Scarton trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Bleu: a Method for Automatic Evaluation of Machine Translation
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
Their papers
- Findings of the 2016 Conference on Machine Translation (2016) — 838 citations
- Findings of the 2015 Workshop on Statistical Machine Translation (2015) — 344 citations
- ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations (2020) — 192 citations
- Multi-level Translation Quality Prediction with QuEst++ (2015) — 140 citations
- Word embeddings and discourse information for Quality Estimation (2016) — 11 citations
- EASSE: Easier Automatic Sentence Simplification Evaluation (2019) — 149 citations
- Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis (2020) — 127 citations
- Searching for Context: a Study on Document-Level Labels for Translation Quality Estimation (2015) — 27 citations
- USHEF and USAAR-USHEF participation in the WMT15 QE shared task (2015) — 9 citations
- Document-level translation quality estimation: exploring discourse and pseudo-references (2014) — 31 citations
- Exploring Consensus in Machine Translation for Quality Estimation (2014) — 8 citations