Introducing Semantic Reader
An AI-Powered Augmented Scientific Reading Application, Now Available in Beta.
Semantic Reader Beta is an augmented reader with the potential to revolutionize scientific reading by making it more accessible and richly contextual.
Observations of scientists reading technical papers showed that readers frequently page back and forth looking for the definitions of terms and mathematical symbols as well as for the details of cited papers. This need to jump around through the paper breaks the flow of paper comprehension.
Semantic Reader provides this information directly in context by dimming unrelated text and providing details in tooltips, and soon will also provide corresponding term definitions. It uses artificial intelligence to understand a document’s structure. Usability studies show readers answered questions requiring deep understanding of paper concepts significantly more quickly with ScholarPhi than with a baseline PDF reader; furthermore, they viewed much less of the paper.
Based on the ScholarPhi research from the Semantic Scholar team at AI2, UC Berkeley and the University of Washington, and supported in part by the Alfred P. Sloan Foundation, the Semantic Reader is now available in beta for a select group of arXiv papers on semanticscholar.org with plans to add additional features and expand coverage soon.
Here are examples of Semantic Reader operating over popular Computer Science papers across various fields. We are incrementally improving, testing, and rolling out features from ScholarPhi in Semantic Reader so stay tuned.
Natural Language Processing
- Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
- ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
- Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
- Long-term Recurrent Convolutional Networks for Visual Recognition and Description
- Image-to-Image Translation with Conditional Adversarial Networks
- Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
- Rethinking the Inception Architecture for Computer Vision
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Semantic Reader is based on research from the Semantic Scholar team at AI2, UC Berkeley and the University of Washington, and supported in part by the Alfred P. Sloan Foundation.
Despite the central importance of research papers to scientific progress, they can be difficult to read. Comprehension is often stymied when the information needed to understand a passage resides somewhere else: in another section, or in another paper. In this work, we envision...
The task of definition detection is important for scholarly papers, because papers often make use of technical terminology that may be unfamiliar to readers. Despite prior work on definition detection, current approaches are far from being accurate enough to use...