Menu
Viewing 121-140 of 247 papers
Clear all
    • arXiv 2018
      Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord
      We present a new question set, text corpus, and baselines assembled to encourage AI research in advanced question answering. Together, these constitute the AI2 Reasoning Challenge (ARC), which requires far more powerful knowledge and reasoning than previous challenges such as SQuAD or SNLI. The ARC…  (More)
    • AAAI 2018
      Tushar Khot, Ashish Sabharwal, and Peter Clark
      We present a new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem. SCITAIL is the first entailment set that is created solely from natural sentences that already exist independently "in the wild" rather than sentences…  (More)
    • AAAI 2018
      Daniel Khashabi, Tushar Khot, Ashish Sabharwal, and Dan Roth
      We propose a novel method for exploiting the semantic structure of text to answer multiple-choice questions. The approach is especially suitable for domains that require reasoning over a diverse set of linguistic constructs but have limited training data. To address these challenges, we present the…  (More)
    • AAAI 2018
      Jonathan Kuck, Ashish Sabharwal, and Stefano Ermon
      Rademacher complexity is often used to characterize the learnability of a hypothesis class and is known to be related to the class size. We leverage this observation and introduce a new technique for estimating the size of an arbitrary weighted set, defined as the sum of weights of all elements in…  (More)
    • AAAI 2018
      Yonatan Bisk, Kevin J. Shih, Yejin Choi, and Daniel Marcu
      In this paper, we study the problem of mapping natural language instructions to complex spatial actions in a 3D blocks world. We first introduce a new dataset that pairs complex 3D spatial operations to rich natural language descriptions that require complex spatial and pragmatic interpretations…  (More)
    • 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)