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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)Expand
Neural Architectures for Named Entity Recognition
Comunicacio presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 de juny 2016.
Hierarchical Attention Networks for Document Classification
Experiments conducted on six large scale text classification tasks demonstrate that the proposed architecture outperform previous methods by a substantial margin. Expand
Relational inductive biases, deep learning, and graph networks
It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Expand
Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters
This work systematically evaluates the use of large-scale unsupervised word clustering and new lexical features to improve tagging accuracy on Twitter and achieves state-of-the-art tagging results on both Twitter and IRC POS tagging tasks. Expand
Transition-Based Dependency Parsing with Stack Long Short-Term Memory
This work was sponsored in part by the U. S. Army Research Laboratory and the NSF CAREER grant IIS-1054319 and the European Commission. Expand
A Simple, Fast, and Effective Reparameterization of IBM Model 2
We present a simple log-linear reparameterization of IBM Model 2 that overcomes problems arising from Model 1’s strong assumptions and Model 2’s overparameterization. Efficient inference, likelihoodExpand
Recurrent Neural Network Grammars
We introduce recurrent neural network grammars, probabilistic models of sentences with explicit phrase structure. We explain efficient inference procedures that allow application to both parsing andExpand
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
A model for constructing vector representations of words by composing characters using bidirectional LSTMs that requires only a single vector per character type and a fixed set of parameters for the compositional model, which yields state- of-the-art results in language modeling and part-of-speech tagging. Expand
A Discriminative Graph-Based Parser for the Abstract Meaning Representation
The first approach to parse sentences into meaning representation, a semantic formalism for which a grow- ing set of annotated examples is available, is introduced, providing a strong baseline for improvement. Expand