Modern machine translation systems are trained on large volumes of parallel data obtained using heuristic methods of bypassing the Internet. The poor quality of the data leads to systematic translation errors, which can be quite noticeable to humans. To fix such errors, human-based models for reranking hypotheses is introduced in this study. In this paper the use of human markup is shown not only to increase the overall quality of the translation but also to significantly reduce the number of systematic translation errors. In addition, the relative simplicity of human markup and its integration in the model training process opens up new opportunities in the field of domain adaptation of translation models for new domains like online retail.

Regularization, robustness and sparsity of probabilistic topic models

2012Journal articleKonstantin Vyacheslavovich Vorontsov, Anna Alexandrovna Potapenko

Computer Research and Modeling

We propose a generalized probabilistic topic model of text corpora which can incorporate heuristics of Bayesian regularization, sampling, frequent parameters update, and robustness in any combinations. Wellknown models PLSA, LDA, CVB0, SWB, and many others can be considered as special cases of the proposed broad family of models. We propose the robust PLSA model and show that it is more sparse and performs better that regularized models like LDA.

Additive regularizarion of topic models with fast text vectorizartion

2020Journal articleIlya Aleksandrovich Irkhin, Victor Gennadyevich Bulatov, Konstantin Vyacheslavovich Vorontsov

Computer Research and Modeling

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Sharpness Estimation of Combinatorial Generalization Ability Bounds for Threshold Decision Rules

2021Journal articleSh. Kh. Ishkina, K. V. Vorontsov

Automation and Remote Control

This article is devoted to the problem of calculating an exact upper bound for the functionals of the generalization ability of a family of one-dimensional threshold decision rules. An algorithm is investigated that solves the stated problem and is polynomial in the total number of samples used for training and validation and in the number of training samples. A theorem is proved for calculating an estimate for the functional of expected overfitting and an estimate for the error rate of the method for minimizing empirical risk on a validation set. The exact bounds calculated using the theorem are compared with the previously known quick-to-compute upper bounds so as to estimate the orders of overestimation of the bounds and to identify the bounds that could be used in real problems.