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Articles by Aleksandr Perevalov
A bird’s-eye view on the evolution of language models for text generation
In this article I would like to share my notes on how language models (LMs) have been developing during the last decades. This text may serve a a gentle introduction and help to understand the conceptual points of LMs throughout their history. It’s worth mentioning that I don’t dive very deep into the implementation details and math behind it, however, the level of description is enough to understand LMs’ evolution properly. What is Language Modeling?
Lingua Franca - Entity-Aware Machine Translation Approach for Question Answering over Knowledge Graphs Original
Towards a lingua franca for knowledge graph question answering systems Machine Translation (MT) can enhance existing Question Answering (QA) systems, which have limited language capabilities, by enabling them to support multiple languages. However, there is one major drawback of MT — often, it fails at translating named entities that are not translatable word-by-word.
Can Machine Translation be a Reasonable Alternative for Multilingual Question Answering Systems over Knowledge Graphs?
Spoiler alert: yes it can! Providing access to information is the main and most important purpose of the Web. Despite available easy-to-use tools (e.g., search engines, question answering) the accessibility is typically limited by the capability of using the English language. In this work, we evaluate Knowledge Graph Question Answering (KGQA) systems that aim at providing natural-language access to data stored in Knowledge Graphs (KG).
Linguistic Features of Human Speech in Dialogues with a Virtual Assistant
Do people talk differently to chatbots than they do to the other people? In this article, I will consider our experiment on statistical analysis of the text of human-human and human-chatbot dialogs. The dialogs were conducted on the topic of frequently asked questions (FAQ) on COVID-19 in the Russian language. The results were processed using quantitative text analysis methods (such groups of metrics as: readability, syntactic complexity, lexical variety).
The New Benchmark for Question Answering over Knowledge Graphs - QALD-9-Plus
And the problem of multilinguality that the benchmark is aiming to solve For heterogeneous user groups (e.g., language, age), the ability to interact with web applications in the same effective way is one of the most important factors in the concept of “accessibility”. This includes Knowledge Graph Question Answering (KGQA) systems, which provide access to data and knowledge from the Semantic Web through a natural language interface.
Hierarchical Classification of Expected Answer Type in Knowledge Graph Question Answering
Thoughts and Theory One of the important steps people use when searching for an answer to a question is to understand what type of answer would suit the best [1]. For example, to the question, “What time is it?” we expect to hear an answer of the “time” type, and to the question, “Where was Ivan Petrov born?” — the answer with the type “city” or “country”. The same is true for Question Answering (QA) systems based on Knowledge Graphs, which aim to find answers to factoid questions.
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