In recent years, distributed representations of inputs have led to performance gains in many applications by allowing statistical information to be shared across inputs. However, the predicted outputs (labels, and more generally structures) are still treated as discrete objects even though outputs are often not discrete units of meaning. In this paper, we present a new formulation for structured prediction where we represent individual labels in a structure as dense vectors and allow semantically similar labels to share parameters. We extend this representation to larger structures by defining compositionality using tensor products to give a natural generalization of standard structured prediction approaches. We define a learning objective for jointly learning the model parameters and the label vectors and propose an alternating minimization algorithm for learning. We show that our formulation outperforms structural SVM baselines in two tasks: multiclass document classification and part-of-speech tagging.
Author Information
Vivek Srikumar (University of Utah)
Christopher D Manning (Stanford University)
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2014 Poster: Learning Distributed Representations for Structured Output Prediction »
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2014 Poster: Simple MAP Inference via Low-Rank Relaxations »
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2013 Poster: Reasoning With Neural Tensor Networks for Knowledge Base Completion »
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2013 Poster: Zero-Shot Learning Through Cross-Modal Transfer »
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2012 Poster: Recursive Deep Learning on 3D Point Clouds »
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2011 Poster: Unfolding Recursive Autoencoders for Paraphrase Detection »
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