Evolution of Gaussian Process kernels for machine translation post-editing effort estimation

In many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is the estimation of the human effort needed to improve a text that has been translated using a machine translation method. Recent advances in this area have shown that Gaussian Processes can be effective in post-editing effort prediction. However, Gaussian Processes require a kernel function to be defined, the choice of which highly influences the quality of the prediction. On the other hand, the extraction of features from the text can be very labor-intensive, although recent advances in sentence embedding have shown that this process can be automated. In this paper, we use a Genetic Programming algorithm to evolve kernels for Gaussian Processes to predict post-editing effort based on sentence embeddings. We show that the combination of evolutionary optimization and Gaussian Processes removes the need for a-priori specification of the kernel choice, and, by using a multi-objective variant of the Genetic Programming approach, kernels that are suitable for predicting several metrics can be learned. We also investigate the effect that the choice of the sentence embedding method has on the kernel learning process.