Dialogue State Tracking (DST) is a core component of virtual assistants such as Alexa or Siri. To accomplish various tasks, these assistants need to support an increasing number of services and APIs. The Schema-Guided State Tracking track of the 8th Dialogue System Technology Challenge highlighted the DST problem for unseen services. The organizers introduced the Schema-Guided Dialogue (SGD) dataset with multi-domain conversations and released a zeroshot dialogue state tracking model. In this work, we propose a GOaL-Oriented Multi-task BERT-based dialogue state tracker (GOLOMB) inspired by architectures for reading comprehension question answering systems. The model queries dialogue history with descriptions of slots and services as well as possible values of slots. This allows to transfer slot values in multi-domain dialogues and have a capability to scale to unseen slot types. Our model achieves a joint goal accuracy of 53.97% on the SGD dataset, outperforming the baseline model.

A numerical simulation of the stationary distribution of H2+ ions in the cathode region of a H2 gas discharge has been carried out using a 'convective-scheme' solution method. A good agreement of calculations with an available experiment is obtained. In the case of constant cross sections the analytical solution of the Boltzmann kinetic equation for the ion distribution function in a cathode region is constructed for arbitrary profiles of an electric field and an ionization source. A simple analytical approximation is proposed for the distribution function of ions in the cathode region of their parent gas discharge.

Multilingual BERT has been shown to generalize well in a zero-shot crosslingual setting. This generalization was measured on POS and NER tasks. We explore the multilingual BERT cross-language transferability on the reading comprehension task. We compare different modes of training of question-answering model for a non-English language using both English and language-specific data. We demonstrate that the model based on multilingual BERT is slightly behind the monolingual BERT-based on Russian data, however, it achieves comparable results with the language-specific variant on Chinese. We also show that training jointly on English data and additional 10,000 monolingual samples allows it to reach the performance comparable to the one trained on monolingual data only.