Liliya Makhmutova

Have you ever noticed that human behavior changes depending on surroundings and people nearby? Do you use different language styles with your friends, colleagues, or boss? As AI impacts more and more on multiple spheres of our life, we want it to be more tuned to our preferences and adapt as a human being. We want AI to respond in appropriate style and show us relevant information. We can argue that there are three levels of personalization: 1. Low level. This level of personalization is focused on low-level grammatical structures (choosing between simple or complicated words, sentence construction, etc.). 2. Medium level. Medium level focuses on semantics and content. It is at this level that AI decides how much information to show to a user and how many words he or she would need for this. 3. High level. At this level AI should be focused on what to say and how often to address a user and customized to user’s preferences. Which means totally or partially understanding of human personality. My Ph.D. will focus on optimizing conversational AI and conversational systems at a low level, and I will try to use some level 2 concepts. So, the main challenge I want to solve at these levels is to provide the user with relevant content. On the fly and in the context of dialogue systems. A good approach to tackling this problem is to use some basic pre-trained model, and fine tuning is so that we learn more and more about the user (similar to recommendation systems). Another approach that can be used is described in the article “PROTOTYPE-K-STYLE”: Create dialogs with style editing in RAM Su et al. We can also consider stylistic customization using GAN, but there are many problems due to the fact that the text is discrete in nature (therefore differentiation is not possible), however there are some heuristics that can solve this problem, so it is worth considering. I also want to look at the explainability of conversational AI, since this topic is closely related to human interaction. I believe that this work will make the interaction of users with dialog systems more convenient and natural, as well as help to find more relevant information easily and faster.