Qingsong Ma

Evaluation & Benchmarks · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 27 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Evaluation & Benchmarks — 59 citations into it; #559 of 3,147 by trust here, #1,132 by citations
• Artificial Intelligence — 53 citations into it; #5,135 of 35,345 by trust here, #7,797 by citations
• Natural Language Processing — 51 citations into it; #1,191 of 5,688 by trust here, #1,779 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Language and Linguistics — 5 citations into it

Whom Qingsong Ma trusts overall

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

1. Yvette Graham
2. Maja Popovic
3. Philipp Koehn
4. Ondrej Bojar
5. Timothy Baldwin
6. Matt Post
7. Gregor Leusch
8. Matthew G. Snover
9. Michael J. Denkowski
10. Matous Machácek

Who trusts Qingsong Ma overall

1. Thibault Sellam
2. Ricardo Rei
3. Sebastian Gehrmann
4. Nitika Mathur
5. Johnny Tian-Zheng Wei
6. Amy Pu
7. Ananya B. Sai
8. Chi-kiu (羅致翹) Lo
9. Weizhe Yuan
10. Markus Freitag

Papers from Qingsong Ma's perspective

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

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

Their papers

• Results of the WMT19 Metrics Shared Task: Segment-Level and Strong MT Systems Pose Big Challenges (2019) — 191 citations
• Results of the WMT18 Metrics Shared Task: Both characters and embeddings achieve good performance (2018) — 105 citations
• Results of the WMT20 Metrics Shared Task (2020) — 170 citations