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Dynamic Entity Representations in Neural Language Models
Yangfeng Ji, Chenhao Tan, Sebastian Martschat, Yejin Choi, Noah A. SmithEMNLP • 2017 Understanding a long document requires tracking how entities are introduced and evolve over time. We present a new type of language model, EntityNLM, that can explicitly model entities, dynamically update their representations, and contextually generate their…Learning a Neural Semantic Parser from User Feedback
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, and Luke ZettlemoyerACL • 2017 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…Semi-supervised sequence tagging with bidirectional language models
Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, and Russell PowerACL • 2017 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…Deep Semantic Role Labeling: What Works and What's Next
Luheng He, Kenton Lee, Mike Lewis, Luke S. ZettlemoyerACL • 2017 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 ZettlemoyerEMNLP • 2017 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 GardnerEMNLP • 2017 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 AngTACL • 2015 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 KushmanEMNLP • 2014 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…