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SciTaiL: A Textual Entailment Dataset from Science Question Answering
We present a new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem. SCITAIL is the first entailment set that is createdExpand
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Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
We present a new question set, text corpus, and baselines assembled to encourage AI research in advanced question answering. Together, these constitute the AI2 Reasoning Challenge (ARC), whichExpand
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Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
We present a new kind of question answering dataset, OpenBookQA, modeled after open book exams for assessing human understanding of a subject. The open book that comes with our questions is a set ofExpand
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Gradient-based boosting for statistical relational learning: The relational dependency network case
Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are graphical modelsExpand
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QASC: A Dataset for Question Answering via Sentence Composition
Composing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition(QASC), thatExpand
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Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions
What capabilities are required for an AI system to pass standard 4th Grade Science Tests? Previous work has examined the use of Markov Logic Networks (MLNs) to represent the requisite backgroundExpand
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Question Answering via Integer Programming over Semi-Structured Knowledge
Answering science questions posed in natural language is an important AI challenge. Answering such questions often requires non-trivial inference and knowledge that goes beyond factoid retrieval.Expand
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Learning Markov Logic Networks via Functional Gradient Boosting
Recent years have seen a surge of interest in Statistical Relational Learning (SRL) models that combine logic with probabilities. One prominent example is Markov Logic Networks (MLNs). While MLNs areExpand
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How creative is your writing? A linguistic creativity measure from computer science and cognitive psychology perspectives
We demonstrate that subjective creativity in sentence-writing can in part be predicted using computable quantities studied in Computer Science and Cognitive Psychology. We introduce a task in which aExpand
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Markov Logic Networks for Natural Language Question Answering
Our goal is to answer elementary-level science questions using knowledge extracted automatically from science textbooks, expressed in a subset of first-order logic. Given the incomplete and noisyExpand
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