Olga Gureenkova


2020

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A New Neural Search and Insights Platform for Navigating and Organizing AI Research
Marzieh Fadaee | Olga Gureenkova | Fernando Rejon Barrera | Carsten Schnober | Wouter Weerkamp | Jakub Zavrel
Proceedings of the First Workshop on Scholarly Document Processing

To provide AI researchers with modern tools for dealing with the explosive growth of the research literature in their field, we introduce a new platform, AI Research Navigator, that combines classical keyword search with neural retrieval to discover and organize relevant literature. The system provides search at multiple levels of textual granularity, from sentences to aggregations across documents, both in natural language and through navigation in a domain specific Knowledge Graph. We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.

2018

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DeepPavlov: Open-Source Library for Dialogue Systems
Mikhail Burtsev | Alexander Seliverstov | Rafael Airapetyan | Mikhail Arkhipov | Dilyara Baymurzina | Nickolay Bushkov | Olga Gureenkova | Taras Khakhulin | Yuri Kuratov | Denis Kuznetsov | Alexey Litinsky | Varvara Logacheva | Alexey Lymar | Valentin Malykh | Maxim Petrov | Vadim Polulyakh | Leonid Pugachev | Alexey Sorokin | Maria Vikhreva | Marat Zaynutdinov
Proceedings of ACL 2018, System Demonstrations

Adoption of messaging communication and voice assistants has grown rapidly in the last years. This creates a demand for tools that speed up prototyping of feature-rich dialogue systems. An open-source library DeepPavlov is tailored for development of conversational agents. The library prioritises efficiency, modularity, and extensibility with the goal to make it easier to develop dialogue systems from scratch and with limited data available. It supports modular as well as end-to-end approaches to implementation of conversational agents. Conversational agent consists of skills and every skill can be decomposed into components. Components are usually models which solve typical NLP tasks such as intent classification, named entity recognition or pre-trained word vectors. Sequence-to-sequence chit-chat skill, question answering skill or task-oriented skill can be assembled from components provided in the library.