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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…Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning
Mohammad Rastegari, Hannaneh Hajishirzi, and Ali FarhadiCVPR • 2015 In this paper we present a bottom-up method to instance level Multiple Instance Learning (MIL) that learns to discover positive instances with globally constrained reasoning about local pairwise similarities. We discover positive instances by optimizing for a…Elementary School Science and Math Tests as a Driver for AI: Take the Aristo Challenge!
Peter ClarkProceedings of IAAI • 2015 While there has been an explosion of impressive, datadriven AI applications in recent years, machines still largely lack a deeper understanding of the world to answer questions that go beyond information explicitly stated in text, and to explain and discuss…Exploring Markov Logic Networks for Question Answering
Tushar Khot, Niranjan Balasubramanian, Eric Gribkoff, Ashish Sabharwal, Peter Clark, and Oren EtzioniEMNLP • 2015 Elementary-level science exams pose significant knowledge acquisition and reasoning challenges for automatic question answering. We develop a system that reasons with knowledge derived from textbooks, represented in a subset of first-order logic. Automatic…Generating Notifications for Missing Actions: Don’t forget to turn the lights off!
Bilge Soran, Ali Farhadi, and Linda ShapiroICCV • 2015 We all have experienced forgetting habitual actions among our daily activities. For example, we probably have forgotten to turn the lights off before leaving a room or turn the stove off after cooking. In this paper, we propose a solution to the problem of…Higher-order Lexical Semantic Models for Non-factoid Answer Reranking
Daniel Fried, Peter Jansen, Gustave Hahn-Powell, Mihai Surdeanu, and Peter ClarkTACL • 2015 Lexical semantic models provide robust performance for question answering, but, in general, can only capitalize on direct evidence seen during training. For example, monolingual alignment models acquire term alignment probabilities from semistructured data…