Simon Mille

Artificial Intelligence · 2010–2022

A researcher in the Science corpus: 12 papers; trusts 48 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 317 citations into it; #30,068 of 35,345 by trust here, #6,330 by citations
• Natural Language Processing — 307 citations into it; #3,318 of 5,688 by trust here, #1,300 by citations
• Evaluation & Benchmarks — 164 citations into it
• Information Systems — 26 citations into it
• Computational Linguistics & Semantics — 7 citations into it
• Molecular Biology — 6 citations into it
• Linguistics — 5 citations into it

Whom Simon Mille trusts overall

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

1. Anya Belz
2. Yvette Graham
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ilya Sutskever
7. Chin-Yew Lin
8. Martha Palmer
9. Paul R. Kingsbury
10. Richard Socher

Who trusts Simon Mille overall

1. Hongmin Wang
2. Sebastian Gehrmann
3. Anya Belz
4. Claire Gardent
5. Ondrej Dusek
6. Anastasia Shimorina
7. David M. Howcroft
8. Thiago Castro Ferreira
9. Amit Moryossef
10. Eleftheria Briakou

Papers from Simon Mille's perspective

Ordered by how strongly the researchers Simon Mille 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. ImageNet classification with deep convolutional neural networks
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Gradient-based learning applied to document recognition

Their papers

• The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021) — 329 citations
• Automatic Construction of Evaluation Suites for Natural Language Generation Datasets (2021) — 27 citations
• GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022) — 43 citations
• The First Multilingual Surface Realisation Shared Task (SR’18): Overview and Evaluation Results (2018) — 65 citations
• Disentangling the Properties of Human Evaluation Methods: A Classification System to Support Comparability, Meta-Evaluation and Reproducibility Testing (2020) — 78 citations
• NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation (2021) — 104 citations
• FORGe at SemEval-2017 Task 9: Deep sentence generation based on a sequence of graph transducers (2017) — 24 citations
• Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definitions (2020) — 263 citations
• The Third Multilingual Surface Realisation Shared Task (SR’20): Overview and Evaluation Results (2020)
• The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020) (2020)
• Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019) (2019)
• Broad Coverage Multilingual Deep Sentence Generation with a Stochastic Multi-Level Realizer (2010) — 61 citations