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Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions
Peter Clark, Oren Etzioni, Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, and Peter TurneyAAAI • 2016 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 background knowledge and interpret test questions, but did not improve upon…Exact Sampling with Integer Linear Programs and Random Perturbations
Carolyn Kim, Ashish Sabharwal, and Stefano ErmonAAAI • 2016 We consider the problem of sampling from a discrete probability distribution specified by a graphical model. Exact samples can, in principle, be obtained by computing the mode of the original model perturbed with an exponentially many i.i.d. random variables…Hierarchical Semi-supervised Classification with Incomplete Class Hierarchies
Bhavana Dalvi, Aditya Mishra, and William W. CohenWSDM • 2016 In an entity classification task, topic or concept hierarchies are often incomplete. Previous work by Dalvi et al. has shown that in non-hierarchical semi-supervised classification tasks, the presence of such unanticipated classes can cause semantic drift for…Keeping AI Legal
Amitai Etzioni and Oren EtzioniVanderbilt • 2016 AI programs make numerous decisions on their own, lack transparency, and may change frequently. Hence, the article shows, unassisted human agents — such as auditors, accountants, inspectors, and police — cannot ensure that AI guided instruments will abide by…Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach
Shuo Yang, Tushar Khot, Kristian Kersting, and Sriraam NatarajanAAAI • 2016 Many real world applications in medicine, biology, communication networks, web mining, and economics, among others, involve modeling and learning structured stochastic processes that evolve over continuous time. Existing approaches, however, have focused on…Selecting Near-Optimal Learners via Incremental Data Allocation
Ashish Sabharwal, Horst Samulowitz, and Gerald TesauroAAAI • 2016 We study a novel machine learning (ML) problem setting of sequentially allocating small subsets of training data amongst a large set of classifiers. The goal is to select a classifier that will give near-optimal accuracy when trained on all data, while also…Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing
Hamid Izadinia, Fereshteh Sadeghi, Santosh Divvala, Hanna Hajishirzi, Yejin Choi, and Ali FarhadiICCV • 2015 We introduce Segment-Phrase Table (SPT), a large collection of bijective associations between textual phrases and their corresponding segmentations. Leveraging recent progress in object recognition and natural language semantics, we show how we can…Solving Geometry Problems: Combining Text and Diagram Interpretation
Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi, Oren Etzioni, and Clint MalcolmEMNLP • 2015 This paper introduces GeoS, the first automated system to solve unaltered SAT geometry questions by combining text understanding and diagram interpretation. We model the problem of understanding geometry questions as submodular optimization, and identify a…Answering Elementary Science Questions by Constructing Coherent Scenes using Background Knowledge
Yang Li and Peter ClarkEMNLP • 2015 Much of what we understand from text is not explicitly stated. Rather, the reader uses his/her knowledge to fill in gaps and create a coherent, mental picture or “scene” depicting what text appears to convey. The scene constitutes an understanding of the text…BDD-Guided Clause Generation
Brian Kell, Ashish Sabharwal, and Willem-Jan van HoeveCPAIOR • 2015 Nogood learning is a critical component of Boolean satisfiability (SAT) solvers, and increasingly popular in the context of integer programming and constraint programming. We present a generic method to learn valid clauses from exact or approximate binary…