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Viewing 41 papers from 2017
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    • SIGMOD Record 2017
      Niket Tandon, Aparna S. Varde, Gerard de Melo
      There is growing conviction that the future of computing depends on our ability to exploit big data on theWeb to enhance intelligent systems. This includes encyclopedic knowledge for factual details, common sense for human-like reasoning and natural language generation for smarter communication…  (More)
    • Award Best Paper Award
      EMNLP 2017
      Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordóñez, Kai-Wei Chang
      Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and visual input but risk inadvertently encoding…  (More)
    • ACL 2017
      Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power
      Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively…  (More)
    • ACL 2017
      Tushar Khot, Ashish Sabharwal, and Peter Clark
      While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to generate semi-structured knowledge for…  (More)
    • ACL 2017
      Niket Tandon, Gerard de Melo, and Gerhard Weikum
      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 distill fine-grained disambiguated…  (More)
    • ACL 2017
      Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, and Luke Zettlemoyer
      We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention. To achieve this, we adapt neural sequence models to map utterances directly to SQL with its…  (More)
    • WWW 2017
      Cuong Xuan Chu, Niket Tandon, and Gerhard Weikum
      Knowledge graphs have become a fundamental asset for search engines. A fair amount of user queries seek information on problem-solving tasks such as building a fence or repairing a bicycle. However, knowledge graphs completely lack this kind of how-to knowledge. This paper presents a method for…  (More)
    • TACL 2017
      Bhavana Dalvi, Niket Tandon, and Peter Clark
      Our goal is to construct a domain-targeted, high precision knowledge base (KB), containing general (subject,predicate,object) statements about the world, in support of a downstream question-answering (QA) application. Despite recent advances in information extraction (IE) techniques, no suitable…  (More)
    • Journal of Ethics 2017
      Amitai Etzioni and Oren Etzioni
      This article reviews the reasons scholars hold that driverless cars and many other AI equipped machines must be able to make ethical decisions, and the difficulties this approach faces. It then shows that cars have no moral agency, and that the term 'autonomous', commonly applied to these machines…  (More)
    • ICLR 2017
      Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi
      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 attention to focus on a small portion of the…  (More)
    • ICLR 2017
      Minjoon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi
      In this paper, we study the problem of question answering when reasoning over multiple facts is required. We propose Query-Reduction Network (QRN), a variant of Recurrent Neural Network (RNN) that effectively handles both short-term (local) and long-term (global) sequential dependencies to reason…  (More)
    • WWW 2017
      Chenyan Xiong, Russell Power and Jamie Callan
      This paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that leverages knowledge graph embedding. Analysis of the query log from our academic search engine, SemanticScholar.org, reveals that a major error source is its inability to understand the meaning of research concepts…  (More)
    • arXiv 2017 Slides
      Peter D. Turney
      While open-domain question answering (QA) systems have proven effective for answering simple questions, they struggle with more complex questions. Our goal is to answer more complex questions reliably, without incurring a significant cost in knowledge resource construction to support the QA. One…  (More)
    • ICRA 2017
      Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph Lim, Abhinav Gupta, Fei-Fei Li, and Ali Farhadi
      Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new goals, and (2) data inefficiency, i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios…  (More)
    • CVPR 2017
      Gunnar A Sigurdsson, Santosh Divvala, Ali Farhadi, and Abhinav Gupta
      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 higher-level constructs such as…  (More)
    • CVPR 2017
      Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Hannaneh Hajishirzi, and Ali Farhadi
      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-modal questions, taken from middle…  (More)
    • CVPR 2017
      Mark Yatskar, Vicente Ordonez, Luke Zettlemoyer, and Ali Farhadi
      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 situation recognition, the task of producing…  (More)
    • CVPR 2017
      Hessam Bagherinezhad, Mohammad Rastegari, and Ali Farhadi
      Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for convolutional neural networks that enables efficient learning and inference. We introduce LCNN, a lookup…  (More)
    • Award Best Paper Honorable Mention
      CVPR 2017
      Joseph Redmon and Ali Farhadi
      We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, is state-of-the-art on standard detection…  (More)
    • SemEval 2017
      Waleed Ammar, Matthew E. Peters, Chandra Bhagavatula, and Russell Power
      This paper describes our submission for the ScienceIE shared task (SemEval-2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several enhancements such as semi-supervised learning via…  (More)
    • Issues in Science and Technology 2017
      Amitai Etzioni and Oren Etzioni
      New technologies often spur public anxiety, but the intensity of concern about the implications of advances in artificial intelligence (AI) is particularly noteworthy. Several respected scholars and technology leaders warn that AI is on the path to turning robots into a master class that will…  (More)
    • JCDL 2017
      Luca Weihs and Oren Etzioni
      Citations implicitly encode a community's judgment of a paper's importance and thus provide a unique signal by which to study scientific impact. Efforts in understanding and refining this signal are reflected in the probabilistic modeling of citation networks and the proliferation of citation-based…  (More)
    • UAI 2017
      Ashish Sabharwal and Hanie Sedghi
      Large scale machine learning produces massive datasets whose items are often associated with a confidence level and can thus be ranked. However, computing the precision of these resources requires human annotation, which is often prohibitively expensive and is therefore skipped. We consider the…  (More)
    • SIGIR 2017
      Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, and Russell Power
      This paper proposes K-NRM, a kernel based neural model for document ranking. Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features…  (More)
    • Nature 2017
      Oren Etzioni
      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 and therefore most highly influential…  (More)
    • VAST 2017 Demo Video
      Nan-Chen Chen and Been Kim
      Developing sophisticated artificial intelligence (AI) systems requires AI researchers to experiment with different designs and analyze results from evaluations (we refer this task as evaluation analysis). In this paper, we tackle the challenges of evaluation analysis in the domain of question…  (More)
    • EMNLP • Workshop on Noisy User-generated Text 2017
      Johannes Welbl, Nelson F. Liu, and Matt Gardner
      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 addresses these problems by leveraging a large…  (More)
    • CoNLL 2017
      Daniel Khashabi, Tushar Khot, Ashish Sabharwal, and Dan Roth
      Question answering (QA) systems are easily distracted by irrelevant or redundant words in questions, especially when faced with long or multi-sentence questions in difficult domains. This paper introduces and studies the notion of essential question terms with the goal of improving such QA solvers…  (More)
    • CoNLL 2017
      Rebecca Sharp, Mihai Surdeanu, Peter Jansen, Marco A. Valenzuela-Escárcega, Peter Clark, and Michael Hammond
      For many applications of question answering (QA), being able to explain why a given model chose an answer is critical. However, the lack of labeled data for answer justifications makes learning this difficult and expensive. Here we propose an approach that uses answer ranking as distant supervision…  (More)
    • CoNLL 2017
      Ivan Vulic, Roy Schwartz, Ari Rappoport, Roi Reichart, and Anna Korhonen
      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-specific context configurations. We…  (More)
    • CoNLL 2017
      Roy Schwartz, Maarten Sap, Ioannis Konstas, Leila Zilles, Yejin Choi, Noah A. Smith
      A writer’s style depends not just on personal traits but also on her intent and mental state. In this paper, we show how variants of the same writing task can lead to measurable differences in writing style. We present a case study based on the story cloze task (Mostafazadeh et al., 2016a), where…  (More)
    • ACL 2017
      Pradeep Dasigi, Waleed Ammar, Chris Dyer, and Eduard Hovy
      Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by…  (More)
    • EMNLP 2017
      Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer
      We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or handengineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions…  (More)
    • EMNLP 2017
      Jayant Krishnamurthy, Pradeep Dasigi, and Matt Gardner
      We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key technical innovations: (1) a grammar for the decoder that only generates well-typed logical forms; and (2) an entity…  (More)
    • EMNLP 2017
      Mark Hopkins, Cristian Petrescu-Prahova, Roie Levin, Ronan Le Bras, Alvaro Herrasti, and Vidur Joshi
      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 transducer cascade as its basic…  (More)
    • EMNLP 2017
      Aaron Sarnat, Vidur Joshi, Cristian Petrescu-Prahova, Alvaro Herrasti, Brandon Stilson, and Mark Hopkins
      We provide a visualization library and web interface for interactively exploring a parse tree or a forest of parses. The library is not tied to any particular linguistic representation, but provides a generalpurpose API for the interactive exploration of hierarchical linguistic structure. To…  (More)
    • Military Review 2017
      Amitai Etzioni and Oren Etzioni
      Autonomous weapons systems and military robots are progressing from science fiction movies to designers' drawing boards, to engineering laboratories, and to the battlefield. These machines have prompted a debate among military planners, roboticists, and ethicists about the development and…  (More)
    • ICCV 2017
      Roozbeh Mottaghi, Connor Schenck, Dieter Fox, Ali Farhadi
      Humans have rich understanding of liquid containers and their contents; for example, we can effortlessly pour water from a pitcher to a cup. Doing so requires estimating the volume of the cup, approximating the amount of water in the pitcher, and predicting the behavior of water when we tilt the…  (More)
    • ICCV 2017
      Yuke Zhu, Daniel Gordon, Eric Kolve, Dieter Fox, Li Fei-Fei, Abhinav Gupta, Roozbeh Mottaghi, Ali Farhadi
      A crucial capability of real-world intelligent agents is their ability to plan a sequence of actions to achieve their goals in the visual world. In this work, we address the problem of visual semantic planning: the task of predicting a sequence of actions from visual observations that transform a…  (More)
    • ACL 2017
      Luheng He, Kenton Lee, Mike Lewis, Luke S. Zettlemoyer
      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, while observing a number of recent…  (More)
    • AAAI 2017
      Matt Gardner and Jayant Krishnamurthy
      Traditional semantic parsers map language onto compositional, executable queries in a fixed schema. This map- ping allows them to effectively leverage the information con- tained in large, formal knowledge bases (KBs, e.g., Freebase) to answer questions, but it is also fundamentally limiting…  (More)