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Pontus Stenetorp
Researcher at University College London
Publications - 88
Citations - 4777
Pontus Stenetorp is an academic researcher from University College London. The author has contributed to research in topics: Computer science & Question answering. The author has an hindex of 25, co-authored 88 publications receiving 3552 citations. Previous affiliations of Pontus Stenetorp include Salesforce.com & University of Tokyo.
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Proceedings Article
brat: a Web-based Tool for NLP-Assisted Text Annotation
TL;DR: The brat rapid annotation tool (BRAT) is introduced, an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology and an evaluation of annotation assisted by semantic class disambiguation on a multicategory entity mention annotation task, showing a 15% decrease in total annotation time.
Proceedings ArticleDOI
Convolutional 2D knowledge graph embeddings
TL;DR: ConvE as mentioned in this paper is a multi-layer convolutional network model for link prediction, which achieves state-of-the-art results for several established datasets, such as Freebase and YAGO3.
Posted Content
Convolutional 2D Knowledge Graph Embeddings
TL;DR: ConvE as discussed by the authors is a multi-layer convolutional network model for link prediction, which achieves state-of-the-art results on several established datasets, such as Freebase and YAGO3.
Journal ArticleDOI
Constructing Datasets for Multi-hop Reading Comprehension Across Documents
TL;DR: Most reading comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document as mentioned in this paper, which limits the model to combine disjoint pieces of textual evidence.
Proceedings ArticleDOI
Dynabench: Rethinking Benchmarking in NLP.
Douwe Kiela,Max Bartolo,Yixin Nie,Divyansh Kaushik,Atticus Geiger,Zhengxuan Wu,Bertie Vidgen,Grusha Prasad,Amanpreet Singh,Pratik Ringshia,Zhiyi Ma,Tristan Thrush,Sebastian Riedel,Zeerak Waseem,Pontus Stenetorp,Robin Jia,Mohit Bansal,Christopher Potts,Adina Williams +18 more
TL;DR: It is argued that Dynabench addresses a critical need in the community: contemporary models quickly achieve outstanding performance on benchmark tasks but nonetheless fail on simple challenge examples and falter in real-world scenarios.