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WebChild 2.0: Fine-Grained Commonsense Knowledge Distillation
Niket Tandon, Gerard de Melo, and Gerhard WeikumACL • 2017 Despite important progress in the area of intelligent systems, most such systems still lack commonsense knowledge that appears crucial for enabling smarter, more human-like decisions. In this paper, we present a system based on a series of algorithms to…AI zooms in on highly influential citations
Oren EtzioniNature • 2017 The number of times a paper is cited is a poor proxy for its impact (see P. Stephan et al. Nature 544, 411–412; 2017). I suggest relying instead on a new metric that uses artificial intelligence (AI) to capture the subset of an author's or a paper's essential…Are You Smarter Than A Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension
Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Hannaneh Hajishirzi, and Ali FarhadiCVPR • 2017 We introduce the task of Multi-Modal Machine Comprehension (M3C), which aims at answering multimodal questions given a context of text, diagrams and images. We present the Textbook Question Answering (TQA) dataset that includes 1,076 lessons and 26,260 multi…Asynchronous Temporal Fields for Action Recognition
Gunnar A Sigurdsson, Santosh Divvala, Ali Farhadi, and Abhinav GuptaCVPR • 2017 Actions are more than just movements and trajectories: we cook to eat and we hold a cup to drink from it. A thorough understanding of videos requires going beyond appearance modeling and necessitates reasoning about the sequence of activities, as well as the…Automatic Selection of Context Configurations for Improved Class-Specific Word Representations
Ivan Vulic, Roy Schwartz, Ari Rappoport, Roi Reichart, and Anna KorhonenCoNLL • 2017 This paper is concerned with identifying contexts useful for training word representation models for different word classes such as adjectives (A), verbs (V), and nouns (N). We introduce a simple yet effective framework for an automatic selection of class…Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers
Mark Hopkins, Cristian Petrescu-Prahova, Roie Levin, Ronan Le Bras, Alvaro Herrasti, and Vidur JoshiEMNLP • 2017 We present an approach for answering questions that span multiple sentences and exhibit sophisticated cross-sentence anaphoric phenomena, evaluating on a rich source of such questions--the math portion of the Scholastic Aptitude Test (SAT). By using a tree…Bidirectional Attention Flow for Machine Comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh HajishirziICLR • 2017 Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query. Recently, attention mechanisms have been successfully extended to MC. Typically these methods use…Commonly Uncommon: Semantic Sparsity in Situation Recognition
Mark Yatskar, Vicente Ordonez, Luke Zettlemoyer, and Ali FarhadiCVPR • 2017 Semantic sparsity is a common challenge in structured visual classification problems; when the output space is complex, the vast majority of the possible predictions are rarely, if ever, seen in the training set. This paper studies semantic sparsity in…Crowdsourcing Multiple Choice Science Questions
Johannes Welbl, Nelson F. Liu, and Matt GardnerEMNLP • Workshop on Noisy User-generated Text • 2017 We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method…Deep Semantic Role Labeling: What Works and What's Next
Luheng He, Kenton Lee, Mike Lewis, Luke S. ZettlemoyerACL • 2017 We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM architecture with constrained decoding…