Browsing by Author "Dyer, Chris"

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  • Improved transition-based parsing by modeling characters instead of words with LSTMs
    Ballesteros, Miguel; Dyer, Chris; Smith, Noah A. (ACL (Association for Computational Linguistics), 2015)
    We present extensions to a continuousstate dependency parsing method that makes it applicable to morphologically rich languages. Starting with a highperformance transition-based parser that uses long short-term memory ...
  • Neural architectures for named entity recognition
    Lample, Guillaume; Ballesteros, Miguel; Subramanian, Sandeep; Kawakami, Kazuya; Dyer, Chris (ACL (Association for Computational Linguistics), 2016)
    State-of-the-art named entity recognition systems/nrely heavily on hand-crafted features and/ndomain-specific knowledge in order to learn/neffectively from the small, supervised training/ncorpora that are available. In ...
  • Recurrent neural network grammars
    Dyer, Chris; Kuncoro, Adhiguna; Ballesteros, Miguel; Smith, Noah A. (ACL (Association for Computational Linguistics), 2016)
    We introduce recurrent neural network grammars,/nprobabilistic models of sentences with/nexplicit phrase structure. We explain efficient/ninference procedures that allow application to/nboth parsing and language modeling. ...
  • Transition-based dependency parsing with heuristic backtracking
    Buckman, Jacob; Ballesteros, Miguel; Dyer, Chris (ACL (Association for Computational Linguistics), 2016)
    We introduce a novel approach to the decoding problem in transition-based parsing: heuristic backtracking. This algorithm uses a series of partial parses on the sentence to locate the best candidate parse, using confidence ...
  • Transition-based dependency parsing with stack long short-term memory
    Dyer, Chris; Ballesteros, Miguel; Ling, W; Matthews, A; Smith, Noah A. (ACL (Association for Computational Linguistics), 2015)
    We propose a technique for learning representations of parser states in transitionbased dependency parsers. Our primary innovation is a new control structure for sequence-to-sequence neural networks— the stack LSTM. Like ...