Award Winning Papers

Learn more about AI2's Lasting Impact Award
Viewing 31-40 of 43 papers
  • AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

    Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian, Matthew Gardner, Sameer SinghEMNLP2019 Neural NLP models are increasingly accurate but are imperfect and opaque---they break in counterintuitive ways and leave end users puzzled at their behavior. Model interpretation methods ameliorate this opacity by providing explanations for specific model…
  • On the Limits of Learning to Actively Learn Semantic Representations

    Omri Koshorek, Gabriel Stanovsky, Yichu Zhou, Vivek Srikumar and Jonathan BerantCoNLL2019
    Best Paper Honorable Mention
    One of the goals of natural language understanding is to develop models that map sentences into meaning representations. However, training such models requires expensive annotation of complex structures, which hinders their adoption. Learning to actively…
  • CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

    Alon Talmor, Jonathan Herzig, Nicholas Lourie, Jonathan BerantNAACL2019 When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background…
  • LSTMs Exploit Linguistic Attributes of Data

    Nelson F. Liu, Omer Levy, Roy Schwartz, Chenhao Tan, Noah A. SmithACL • RepL4NLP Workshop2018 While recurrent neural networks have found success in a variety of natural language processing applications, they are general models of sequential data. We investigate how the properties of natural language data affect an LSTM's ability to learn a…
  • Deep Contextualized Word Representations

    Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke ZettlemoyerNAACL2018 We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are…
  • Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

    Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordóñez, Kai-Wei ChangEMNLP2017 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…
  • Bidirectional Attention Flow for Machine Comprehension

    Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh HajishirziICLR2017 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…
  • YOLO9000: Better, Faster, Stronger

    Joseph Redmon, Ali FarhadiCVPR2017 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…
  • Toward a Taxonomy and Computational Models of Abnormalities in Images

    Babak Saleh, Ahmed Elgammal, Jacob Feldman, and Ali FarhadiAAAI2016 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…
  • XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

    Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali FarhadiECCV2016 We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32x memory saving. In XNOR-Networks, both the…