Elizaveta Goncharova


2020

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On a Chatbot Navigating a User through a Concept-Based Knowledge Model
Boris Galitsky | Dmitry Ilvovsky | Elizaveta Goncharova
Proceedings of Workshop on Natural Language Processing in E-Commerce

Information retrieval chatbots are widely used as assistants, to help users formulate their requirements about the products they want to purchase, and navigate to the set of items that satisfies their requirements in the best way. The work of the modern chatbots is based mostly on the deep learning theory behind the knowledge model that can improve the performance of the system. In our work, we are developing a concept-based knowledge model that encapsulates objects and their common descriptions. The leveraging of the concept-based knowledge model allows the system to refine the initial users’ requests and lead them to the set of objects with the maximal variability of parameters that matters less to them. Introducing the additional textual characteristics allows users to formulate their initial query as a phrase in natural language, rather than as some standard request in the form of, “Attribute - value”.

2019

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On a Chatbot Conducting Dialogue-in-Dialogue
Boris Galitsky | Dmitry Ilvovsky | Elizaveta Goncharova
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

We demo a chatbot that delivers content in the form of virtual dialogues automatically produced from plain texts extracted and selected from documents. This virtual dialogue content is provided in the form of answers derived from the found and selected documents split into fragments, and questions are automatically generated for these answers.

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On a Chatbot Providing Virtual Dialogues
Boris Galitsky | Dmitry Ilvovsky | Elizaveta Goncharova
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

We present a chatbot that delivers content in the form of virtual dialogues automatically produced from the plain texts that are extracted and selected from the documents. This virtual dialogue content is provided in the form of answers derived from the found and selected documents split into fragments, and questions that are automatically generated for these answers based on the initial text.