Artem Popov


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Time-Efficient Code Completion Model for the R Programming Language
Artem Popov | Dmitrii Orekhov | Denis Litvinov | Nikolay Korolev | Gleb Morgachev
Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)

In this paper we present a deep learning code completion model for the R language. We introduce several techniques to utilize language modeling based architecture in the code completion task. With these techniques, the model requires low resources, but still achieves high quality. We also present an evaluation dataset for the R language completion task. Our dataset contains multiple autocompletion usage contexts that provides robust validation results. The dataset is publicly available.


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Unsupervised dialogue intent detection via hierarchical topic model
Artem Popov | Victor Bulatov | Darya Polyudova | Eugenia Veselova
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

One of the challenges during a task-oriented chatbot development is the scarce availability of the labeled training data. The best way of getting one is to ask the assessors to tag each dialogue according to its intent. Unfortunately, performing labeling without any provisional collection structure is difficult since the very notion of the intent is ill-defined. In this paper, we propose a hierarchical multimodal regularized topic model to obtain a first approximation of the intent set. Our rationale for hierarchical models usage is their ability to take into account several degrees of the dialogues relevancy. We attempt to build a model that can distinguish between subject-based (e.g. medicine and transport topics) and action-based (e.g. filing of an application and tracking application status) similarities. In order to achieve this, we divide set of all features into several groups according to part-of-speech analysis. Various feature groups are treated differently on different hierarchy levels.