Featured Demos
Modular QA answers questions by breaking them down into a series of smaller, more specific ones. This produces answers in a human-like way that's more explainable than black-box systems. | AristoModularQA is a neuro-symbolic question-answering system that answers complex questions by asking a series of sub-questions to existing simpler QA systems or symbolic modules. It explains each of its reasoning steps in language, in terms of a simple question and its answer as produced by a simpler model or a math… more
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Uncovering stereotypical biases via underspecified questions | AristoThis work focuses specifically on identifying biases in question answering (QA) models. If these models are blindly deployed in real-life settings, the biases within these models could cause real harm, which raises the question; how extensive are social stereotypes in question-answering models?
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Evaluating neural toxic degeneration in language models | MosaicIn new joint work at AI2 and UW, we study how often popular NLP components produce problematic content, what might trigger this neural toxic degeneration from a given system, and whether or not it can be successfully avoided. We also study how much toxicity is present in the web text that these systems learned… more
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Find out whether scientific research supports or refutes a given claim | Semantic ScholarOur 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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Crossing format boundaries with a single QA system | AristoUnifiedQA 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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Exploring the evolving network of science in CORD-19 | Semantic ScholarUse 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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Transformers as Soft Reasoners over Language | AristoRuleTaker determines whether statements are True or False based on rules given in natural language.
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Several demos of a variety of popular computer vision models | PRIORThe 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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Try the QDMR CopyNet parser | AI2 Israel, Question UnderstandingLive 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… more
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A Framework for Explaining Predictions of NLP Models | AllenNLP, AI2 IrvineThe 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… more
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