Chris Dyer

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Noah Ashton Smith153
Christopher D. Manning134
Salim Roukos122
Todd Ward119
Wei-Jing Zhu118
113Noah A. Smith
64Philip Blunsom
62Graham Neubig
60Kevin Gimpel
54Miguel Ballesteros

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1997
2017
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Moses: Open Source Toolkit for Statistical Machine Translation
We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training,… (More)
Neural Architectures for Named Entity Recognition
State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available. In this paper, we introduce two new neural architectures—one based on bidirectional LSTMs and conditional random fields, and the other that… (More)
Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters
We consider the problem of part-of-speech tagging for informal, online conversational text. We systematically evaluate the use of large-scale unsupervised word clustering and new lexical features to improve tagging accuracy. With these features, our system achieves state-of-the-art tagging results on both Twitter and IRC POS tagging tasks; Twitter tagging… (More)
A Discriminative Graph-Based Parser for the Abstract Meaning Representation
Meaning Representation (AMR) is a semantic formalism for which a growing set of annotated examples is available. We introduce the first approach to parse sentences into this representation , providing a strong baseline for future improvement. The method is based on a novel algorithm for finding a maximum spanning, connected subgraph, embedded within a… (More)
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs. Relative to traditional word representation models that have independent vectors for each word type, our model requires only a single vector per character type and a fixed set of parameters for the compositional model. Despite the… (More)
Better Hypothesis Testing for Statistical Machine Translation: Controlling for Optimizer Instability
In statistical machine translation, a researcher seeks to determine whether some innovation (e.g., a new feature, model, or inference algorithm) improves translation quality in comparison to a baseline system. To answer this question, he runs an experiment to evaluate the behavior of the two systems on held-out data. In this paper, we consider how to make… (More)
Transition-Based Dependency Parsing with Stack Long Short-Term Memory
We propose a technique for learning representations of parser states in transition-based dependency parsers. Our primary innovation is a new control structure for sequence-to-sequence neural networks— the stack LSTM. Like the conventional stack data structures used in transition-based parsing, elements can be pushed to or popped from the top of the stack in… (More)
Joshua: An Open Source Toolkit for Parsing-Based Machine Translation
We describe Joshua, an open source toolkit for statistical machine translation. Joshua implements all of the algorithms required for synchronous context free grammars (SCFGs): chart-parsing, n-gram language model integration, beam-and cube-pruning, and k-best extraction. The toolkit also implements suffix-array grammar extraction and minimum error rate… (More)
A Simple, Fast, and Effective Reparameterization of IBM Model 2
We present a simple log-linear reparame-terization of IBM Model 2 that overcomes problems arising from Model 1's strong assumptions and Model 2's overparame-terization. Efficient inference, likelihood evaluation, and parameter estimation algorithms are provided. Training the model is consistently ten times faster than Model 4. On three large-scale… (More)
Improving Vector Space Word Representations Using Multilingual Correlation
The distributional hypothesis of Harris (1954), according to which the meaning of words is evidenced by the contexts they occur in, has motivated several effective techniques for obtaining vector space semantic representations of words using unannotated text corpora. This paper argues that lexico-semantic content should additionally be invariant across… (More)