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Team
Online Learning for Inexact Hypergraph Search
Hao Zhang
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Liang Huang
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Kai Zhao
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Ryan McDonald
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Paper Details:
Month: October
Year: 2013
Location: Seattle, Washington, USA
Venue:
EMNLP |
SIG: SIGDAT
Citations
URL
Jointly or Separately: Which is Better for Parsing Heterogeneous Dependencies?
Meishan Zhang
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Wanxiang Che
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Yanqiu Shao
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Ting Liu
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Max-Violation Perceptron and Forced Decoding for Scalable MT Training
Heng Yu
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Liang Huang
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Haitao Mi
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Kai Zhao
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Greed is Good if Randomized: New Inference for Dependency Parsing
Yuan Zhang
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Tao Lei
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Regina Barzilay
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Tommi Jaakkola
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Search-Aware Tuning for Machine Translation
Lemao Liu
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Liang Huang
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Bi-directional Attention with Agreement for Dependency Parsing
Hao Cheng
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Hao Fang
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Xiaodong He
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Jianfeng Gao
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Li Deng
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Making Dependency Labeling Simple, Fast and Accurate
Tianxiao Shen
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Tao Lei
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Regina Barzilay
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Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees
Yuan Zhang
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Tao Lei
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Regina Barzilay
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Tommi Jaakkola
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Amir Globerson
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Unsupervised Dependency Parsing with Transferring Distribution via Parallel Guidance and Entropy Regularization
Xuezhe Ma
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Fei Xia
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Low-Rank Tensors for Scoring Dependency Structures
Tao Lei
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Yu Xin
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Yuan Zhang
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Regina Barzilay
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Tommi Jaakkola
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Enforcing Structural Diversity in Cube-pruned Dependency Parsing
Hao Zhang
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Ryan McDonald
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Hierarchical MT Training using Max-Violation Perceptron
Kai Zhao
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Liang Huang
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Haitao Mi
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Abe Ittycheriah
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Graph-based Dependency Parsing with Bidirectional LSTM
Wenhui Wang
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Baobao Chang
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http://stp.lingfil.uu.se//nivre/research/Penn2Malt.html
Field Of Study
Task
Tagging
Machine Translation
Language
Multilingual
Chinese
English
Japanese
Spanish
Arabic
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