Portfolio
Teaching
Autumn 2023 Python – MA program
Higher School of EconomicsAutumn 2023 Digital Literacy – BA program
Higher School of Economics
Recent Projects
EMPI AI
A mobile app for supporting inclusion of people with Autism Spectrum Disorder. Current state: prototyping.
Learn moreThe Black Box
A popular science podcast about Artificial Intelligence for Russian-speaking audience.
Learn moreText-to-Image XAI
Experiments on text-to-image AI that combine knowledge from linguistics and computing. A contribution to the black box problem
Learn more
Selected Papers
Towards Building a Mobile App for People on the Spectrum
Victoria Firsanova
Companion Proceedings of the ACM Web Conference 2023, Austin, TX, USA.
The inclusion of autistic people can be augmented by a mobile app that provides information without a human mediator making information perception more liberating for people in the spectrum. This paper is an overview of a doctoral work dedicated to the development of a web-based mobile tool for supporting the inclusion of people on the autism spectrum. The work includes UX/UI research conducted with psychiatry experts, web information retrieval study and neural question-answering research. Currently, the study results comprise several mobile app layouts, a retriever-reader model design and fine-tuned neural network for extractive question-answering. Source code and other resources are available at https://github.com/vifirsanova/empi.Two Approaches to Building Dialogue Systems for People on the Spectrum
Victoria Firsanova
Data-Centric AI Workshop, 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Sydney, Australia.
The paper presents a study on combining model- and data-centric approaches to building a question answering system for inclusion of people with autism spectrum disorder. The study shows that applying sequentially model- and data-centric approaches might allow achieving higher metric scores on closed-domain lowresourced datasets.Transformer Models for Question Answering on Autism Spectrum Disorder QA Dataset
Victoria Firsanova
Springer International Publishings, Digital Transformation and Global Society: 6th International Conference, St. Petersburg, Russia.
Question answering (QA) Transformer-based models might become efficient in inclusive education. For example, one can test and tune such models with small closed-domain datasets before the implementation of a new system in an inclusive organization. However, studies in the sociomedical domain show that such models can be unpredictable. They can mislead a user or evoke aversive emotional states. The paper addresses the problem of investigating safety-first QA models that would generate user-friendly outputs. The study aims to analyze the performance of SOTA Transformer-based QA models on a custom dataset collected by the author of the paper. The dataset contains 1 134 question-answer pairs about autism spectrum disorders (ASD) in Russian. The study presents the validation and evaluation of extractive and generative QA models. The author used transfer learning techniques to investigate domain-specific QA properties and suggest solutions that might provide higher QA efficiency in the inclusion. The study shows that although generative QA models can misrepresent facts and generate false tokens, they might bring diversity in the system outputs and make the automated QA more user-friendly for younger people. Although extractive QA is more reliable, according to the metric scores presented in this study, such models might be less efficient than generative ones. The principal conclusion of the study is that a combination of generative and extractive approaches might lead to higher efficiency in building QA systems for inclusion. However, the performance of such combined systems in the inclusion is yet to be investigated.
