Published November 2, 2019 | Version v1
Conference paper Open

Exploring Kernel Functions in the Softmax Layer for Contextual Word Classification

  • 1. Human Language Technology and Pattern Recognition Group, Computer Science Department, RWTH Aachen University, D-52056 Aachen, Germany


Prominently used in support vector machines and logistic re-gressions, kernel functions (kernels) can implicitly map data points into high dimensional spaces and make it easier to learn complex decision boundaries. In this work, by replacing the inner product function in the softmax layer, we explore the use of kernels for contextual word classification. In order to compare the individual kernels, experiments are conducted on standard language modeling and machine translation tasks. We observe a wide range of performances across different kernel settings. Extending the results, we look at the gradient properties, investigate various mixture strategies and examine the disambiguation abilities.



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