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Reranking Hypotheses in Translation Models Using Human Markup

2024Journal articleКонстантин Вячеславович Воронцов

Journal of Computer and Systems Sciences International

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.
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Incremental Learning of Topic Models for Finding Trend Topics in Scientific Publications

2022Journal articleКонстантин Вячеславович Воронцов

Doklady Mathematics

With a soaring number of scientific publications and rapid emergence of new directions and approaches, the scientific community faces the task of timely identification of trends. By a trend, we mean a semantically homogeneous topic characterized by a steady lexical kernel and a sharp, often exponential increase in the number of publications [1]. Examples of trends in machine learning are “LSTM,” “deep learning,” “word2vec,” “BERT,” and “fake news detection.” For real-time detection of trend topics from a stream of scientific publications, we use incremental methods of probabilistic topic modeling. An ARTM-based approach to early trend detection has been shown to outperform popular classical and neural network approaches to this task. A dataset of 91 trends for performance evaluation has been manually collected and made available for public use.

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Improving the Quality of Machine Translation Using the Reverse Model

2022Journal articleКонстантин Вячеславович Воронцов

Automation and Remote Control

Machine translation is a natural language text processing task that aims to automatically translate input text from one language into another language. The currently known machine translation models show a fairly high quality of translation between large languages, but for smaller language areas, represented by less data, the problem is still not solved. Different methods are used to deal with various errors in automatic translation systems. This paper discusses approaches that use translation models of reverse language directions and improve consistency between translations of the same text using direct and reverse translation models. The paper presents a general theoretical justification for such methods in terms of solving the likelihood maximization problem and also proposes a method for stable training of modern models using cyclic translations.
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Verification of communicative types in the judicial public space of media discourse in the USA, Kazakhstan and Russia as a psycholinguistic marker of fact-checking

2023Journal articleКонстантин Вячеславович Воронцов

Revista Amazonia Investiga

Modern psycholinguistic research and fact-checking actively explore the space of media discourse. However, the representation of the judicial space in the mass media has not been sufficiently studied due to the peculiarities of communicative behavior in the judicial and legal space of the ethno-socius and the attitude to the judiciary. The authors hypothesize that the differences in public behavior in court and the coverage of the work of courts in the American, Kazakh and Russian media are due to the socio-cultural features of the phenomena of judicial and legal communication in public space under the influence of established traditions in such coordinate systems as “person – judicial system”, “openness – closeness of society”, “unity – disunity of society”, “accessibility – stigmatization”, “court – journalistic investigation”, etc. The results confirm the hypothesis of the authors' team, revealing the difference in the perception of the judicial system in the USA, Kazakhstan and Russia, illustrating the "rejection" of the Soviet and post-Soviet stigmatization of the judicial and legal space by the Kazakh society towards democratic norms. The prospects of the study are related to the subsequent development of an automatic system for evaluating speech behavior strategies in court and their coverage in the media as a category of fact-checking.
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Hierarchical Interpretable Topical Embeddings for Exploratory Search and Real-Time Document Tracking

2020Journal articleКонстантин Вячеславович Воронцов

International Journal of Embedded and Real-Time Communication Systems

Real-time monitoring of scientific papers and technological news requires fast processing of complicated search demands motivated by thematically relevant information acquisition. For this case, the authors develop an exploratory search engine based on probabilistic hierarchical topic modeling. Topic model gives a low dimensional sparse interpretable vector representation (topical embedding) of a text, which is used for ranking documents by their similarity to the query. They explore several ways of comparing topical vectors including searching with thematically homogeneous text segments. Topical hierarchies are built using the regularized EM-algorithm from BigARTM project. The topic-based search achieves better precision and recall than other approaches (TF-IDF, fastText, LSTM, BERT) and even human assessors who spend up to an hour to complete the same search task. They also discover that blending hierarchical topic vectors with neural pretrained embeddings is a promising way of enriching both models that helps to get precision and recall higher than 90
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Regularization, robustness and sparsity of probabilistic topic models

2012Journal articleКонстантин Вячеславович Воронцов

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.
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Mining Ethnic Content Online with Additively Regularized Topic Models

2016Journal articleКонстантин Вячеславович Воронцов

Computación y Sistemas

Social studies of the Internet have adopted large-scale text mining for unsupervised discovery of topics related to specific subjects. A recently developed approach to topic modeling, additive regularization of topic models (ARTM), provides fast inference and more control over the topics with a wide variety of possible regularizers than developing LDA extensions. We apply ARTM to mining ethnic-related content from Russian-language blogosphere, introduce a new combined regularizer, and compare models derived from ARTM with LDA. We show with human evaluations that ARTM is better for mining topics on specific subjects, finding more relevant topics of higher or comparable quality.
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Splitting and similarity phenomena in the sets of classifiers and their effect on the probability of overfitting

2009Journal articleКонстантин Вячеславович Воронцов

Pattern Recognition and Image Analysis

It is shown that computationally tight bounds for the probability of overfitting can be obtained only by simultaneous consideration of the following two properties of classifier sets: splitting into error levels and similarity of classifiers. For a set consisting of only two classifiers, an exact bound is obtained for the probability of overfitting. This is the simplest learning task that exhibits overfitting and the effects of splitting and similarity, which reduce the probability of overfitting. For a more complex case—a chain of classifiers—an experiment is carried out in which the effects of splitting and similarity are estimated separately. It is shown that reasonably low probabilities of overfitting can be obtained only for the sets that possess both properties.
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Exact combinatorial bounds on the probability of overfitting for empirical risk minimization

2010Journal articleКонстантин Вячеславович Воронцов

Pattern Recognition and Image Analysis

Three general methods for obtaining exact bounds on the probability of overfitting are proposed within statistical learning theory: a method of generating and destroying sets, a recurrent method, and a blockwise method. Six particular cases are considered to illustrate the application of these methods. These are the following model sets of predictors: a pair of predictors, a layer of a Boolean cube, an interval of a Boolean cube, a monotonic chain, a unimodal chain, and a unit neighborhood of the best predictor. For the interval and the unimodal chain, the results of numerical experiments are presented that demonstrate the effects of splitting and similarity on the probability of overfitting.
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Combinatorial probability and the tightness of generalization bounds

2008Journal articleКонстантин Вячеславович Воронцов

Pattern Recognition and Image Analysis

Accurate prediction of the generalization ability of a learning algorithm is an important problem in computational learning theory. The classical Vapnik-Chervonenkis (VC) generalization bounds are too general and therefore overestimate the expected error. Recently obtained data-dependent bounds are still overestimated. To find out why the bounds are loose, we reject the uniform convergence principle and apply a purely combinatorial approach that is free of any probabilistic assumptions, makes no approximations, and provides an empirical control of looseness. We introduce new data-dependent complexity measures: a local shatter coefficient and a nonscalar local shatter profile , which can give much tighter bounds than the classical VC shatter coefficient . An experiment on real datasets shows that the effective local measures may take very small values; thus, the effective local VC dimension takes values in [0, 1] and therefore is not related to the dimension of the space.

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