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    • 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)