Papers

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Viewing 291-295 of 295 papers
  • Deep Semantic Role Labeling: What Works and What's Next

    Luheng He, Kenton Lee, Mike Lewis, Luke S. ZettlemoyerACL2017 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…
  • End-to-end Neural Coreference Resolution

    Kenton Lee, Luheng He, Mike Lewis, and Luke ZettlemoyerEMNLP2017 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…
  • Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

    Jayant Krishnamurthy, Pradeep Dasigi, and Matt GardnerEMNLP2017 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…
  • Parsing Algebraic Word Problems into Equations

    Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas AngTACL2015 This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and score their likelihood by learning local and global…
  • Learning to Solve Arithmetic Word Problems with Verb Categorization

    Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, and Nate KushmanEMNLP2014 This paper presents a novel approach to learning to solve simple arithmetic word problems. Our system, ARIS, analyzes each of the sentences in the problem statement to identify the relevant variables and their values. ARIS then maps this information into an…