Nitika Mathur

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 6 papers; trusts 55 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it; #6,517 of 35,345 by trust here, #7,657 by citations
• Evaluation & Benchmarks — 81 citations into it; #650 of 3,147 by trust here, #1,096 by citations
• Natural Language Processing — 79 citations into it; #1,834 of 5,688 by trust here, #2,106 by citations
• Information Systems — 10 citations into it; #1,824 of 8,771 by trust here, #1,542 by citations
• Machine Translation — 9 citations into it
• Statistics and Probability — 7 citations into it

Whom Nitika Mathur trusts overall

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

1. Philipp Koehn
2. Yvette Graham
3. Matt Post
4. Ondrej Bojar
5. Chi-kiu (羅致翹) Lo
6. Qingsong Ma
7. Peter J. Rousseeuw
8. Timothy Baldwin
9. Ricardo Rei
10. Rand R. Wilcox

Who trusts Nitika Mathur overall

1. Ricardo Rei
2. Tom Kocmi
3. Markus Freitag
4. Ananya B. Sai
5. Johnny Tian-Zheng Wei
6. Yvette Graham
7. Ondrej Bojar
8. Jungo Kasai
9. Christian Federmann
10. Sebastian Gehrmann

Papers from Nitika Mathur's perspective

Ordered by how strongly the researchers Nitika Mathur 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Bleu: a Method for Automatic Evaluation of Machine Translation
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet classification with deep convolutional neural networks
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Gradient-based learning applied to document recognition

Their papers

• Accurate Evaluation of Segment-level Machine Translation Metrics (2015) — 116 citations
• Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust (2022) — 351 citations
• Results of the WMT20 Metrics Shared Task (2020) — 170 citations
• Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics (2020) — 308 citations
• Sequence Effects in Crowdsourced Annotations (2017) — 13 citations
• Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain (2021) — 206 citations