Featured Demos

Viewing 1-10 of 21 demos
  • SciFact social share card
    Find out whether scientific research supports or refutes a given claim | Semantic Scholar

    Our fact verification demo was built using the SciFact dataset, a collection of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.

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  • UnifiedQA screenshot
    Crossing format boundaries with a single QA system | Aristo

    UnifiedQA is a single pre-trained QA model that performs surprisingly well across 17 QA datasets spanning 4 diverse formats. Fine-tuning UnifiedQA into specialized models results in a new state-of-the-art on 6 datasets, establishing this model as a strong starting point for building QA systems.

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  • SPIKE-CORD Demo Image
    Extractive search over CORD-19 with 3 powerful query modes | AI2 Israel, DIY Information Extraction

    SPIKE-CORD is powerful sentence-level, context-aware, and linguistically informed extractive search system for exploring the CORD-19 corpus.

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  • SciSight logo
    Exploring the evolving network of science in CORD-19 | Semantic Scholar

    Use our exploratory search tools to find out what groups are working on what directions, see how biomedical concepts interact and evolve over time, and discover new connections.

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  • RuleTaker demo logo
    Transformers as Soft Reasoners over Language | Aristo

    RuleTaker determines whether statements are True or False based on rules given in natural language.

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  • Computer Vision Explorer: Try and compare a variety of popular computer vision models.
    Several demos of a variety of popular computer vision models | PRIOR

    The Computer Vision Explorer lets you try and compare a variety of popular computer vision models related to recognition, vision and language, human-centric vision, and scene geometry tasks. Use our example images or try with your own.

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  • Break QDMR representation
    Try the QDMR CopyNet parser | AI2 Israel, Question Understanding

    Live demo of the QDMR CopyNet parser from the paper Break It Down: A Question Understanding Benchmark (TACL 2020). The parser receives a natural language question as input and returns its Question Decomposition Meaning Representation (QDMR). Each step in the decomposition constitutes a subquestion necessary to answer the original question. More info: https://allenai.github.io/Break/

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  • AllenNLP Interpret text image
    A Framework for Explaining Predictions of NLP Models | AllenNLP, AI2 Irvine

    The AllenNLP Interpret toolkit makes it easy to apply gradient-based saliency maps and adversarial attacks to new models, as well as develop new interpretation methods. AllenNLP Interpret contains three components: a suite of interpretation techniques applicable to most models, APIs for developing new interpretation methods (e.g., APIs to obtain input gradients), and reusable front-end components for visualizing the interpretation results.

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  • SUPP.AI - Discover Supplement-Drug Interactions

    SUPP.AI

    Prototype
    Search over our AI-curated corpus of 1,923 supplements, 2,727 drugs, and 55,946 interactions | Semantic Scholar

    SUPP.AI automatically extracts evidence of supplement and drug interactions from the scientific literature. Search for all known potential interactions with a particular agent, or look up the evidence for a specific interaction.

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  • Crowd Sense: Helps us Better Define Common Sense
    Interactive common sense | AI2 Israel, AI Gamification

    CrowdSense is an interactive effort to better understand what types of questions people consider to be common sense.

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