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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…Situation Recognition: Visual Semantic Role Labeling for Image Understanding
Mark Yatskar, Luke Zettlemoyer, and Ali FarhadiCVPR • 2016 This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e.g., clipping), (2) the participating actors, objects, substances, and locations (e.g., man, shears…Stating the Obvious: Extracting Visual Common Sense Knowledge
Mark Yatskar, Vicente Ordonez, and Ali FarhadiNAACL • 2016 Obtaining common sense knowledge using current information extraction techniques is extremely challenging. In this work, we instead propose to derive simple common sense statements from fully annotated object detection corpora such as the Microsoft Common…Toward a Taxonomy and Computational Models of Abnormalities in Images
Babak Saleh, Ahmed Elgammal, Jacob Feldman, and Ali FarhadiAAAI • 2016 The human visual system can spot an abnormal image, and reason about what makes it strange. This task has not received enough attention in computer vision. In this paper we study various types of atypicalities in images in a more comprehensive way than has…Toward Automatic Bootstrapping of Online Communities Using Decision-theoretic Optimization
Shih-Wen Huang, Jonathan Bragg, Isaac Cowhey, Oren Etzioni, and Daniel S. WeldCSCW • 2016 Successful online communities (e.g., Wikipedia, Yelp, and StackOverflow) can produce valuable content. However, many communities fail in their initial stages. Starting an online community is challenging because there is not enough content to attract a…Unsupervised Deep Embedding for Clustering Analysis
Junyuan Xie, Ross Girshick, and Ali FarhadiICML • 2016 Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose…"What happens if..." Learning to Predict the Effect of Forces in Images
Roozbeh Mottaghi, Mohammad Rastegari, Abhinav Gupta, and Ali FarhadiECCV • 2016 What happens if one pushes a cup sitting on a table toward the edge of the table? How about pushing a desk against a wall? In this paper, we study the problem of understanding the movements of objects as a result of applying external forces to them. For a…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…