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Probabilistic Models for Learning a Semantic Parser Lexicon
Jayant KrishnamurthyNAACL • 2016 We introduce several probabilistic models for learning the lexicon of a semantic parser. Lexicon learning is the first step of training a semantic parser for a new application domain and the quality of the learned lexicon significantly affects both the…Question Answering via Integer Programming over Semi-Structured Knowledge
Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Peter Clark, Oren Etzioni, and Dan RothIJCAI • 2016 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. Yet, most systems for this task are based on relatively shallow…Semantic Parsing to Probabilistic Programs for Situated Question Answering
Jayant Krishnamurthy, Oyvind Tafjord, and Aniruddha KembhaviEMNLP • 2016 Situated question answering is the problem of answering questions about an environment such as an image or diagram. This problem requires jointly interpreting a question and an environment using background knowledge to select the correct answer. We present…What's in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams
Peter Jansen, Niranjan Balasubramanian, Mihai Surdeanu, and Peter ClarkCOLING • 2016 QA systems have been making steady advances in the challenging elementary science exam domain. In this work, we develop an explanation-based analysis of knowledge and inference requirements, which supports a fine-grained characterization of the challenges. In…My Computer is an Honor Student — but how Intelligent is it? Standardized Tests as a Measure of AI
Peter Clark and Oren EtzioniAI Magazine • 2016 Given the well-known limitations of the Turing Test, there is a need for objective tests to both focus attention on, and measure progress towards, the goals of AI. In this paper we argue that machine performance on standardized tests should be a key component…Closing the Gap Between Short and Long XORs for Model Counting
Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, and Stefano ErmonAAAI • 2016 Many recent algorithms for approximate model counting are based on a reduction to combinatorial searches over random subsets of the space defined by parity or XOR constraints. Long parity constraints (involving many variables) provide strong theoretical…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…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…