Андрощук, МаксимКирієнко, Оксана2021-01-082021-01-082020Андрощук М. В. Порівняння сервісів для створення питально-відповідальних систем / Андрощук М. В., Кирієнко О. В. // Наукові записки НаУКМА. Комп'ютерні науки. - 2020. - Т. 3. - С. 132-137.2617-3808https://doi.org/10.18523/2617-3808.2020.3.132-137https://ekmair.ukma.edu.ua/handle/123456789/19170У статті розглянуто актуальність питально-відповідальних систем, сервіси для створення питально-відповідальних систем Dialogflow, IBM Watson Assistant, Microsoft QnA Maker, LUIS. Наведено вимоги для роботи та особливості створення питально-відповідальної системи в кожному сервісі. Робота може бути цікавою для дослідників у галузі питально-відповідальних систем і хмарних сервісів.Question-answering systems are widely used nowadays. They allow automating interaction between people. Common solutions include client support, recommendations, and order processing. A question-answering system is a system that answers natural language questions using a structured database or natural language document collection. It can be considered as the development of information retrieval. Three are different services for creating question-answering systems. In this work selected Dialogflow, IBM Watson Assistant, Microsoft QnA Maker, and LUIS. Each service has its advantages and disadvantages. The main concepts of Dialogflow are agents, intents, entities, context. An agent is a virtual agent with a natural language understanding that handles conversations with users. An intent represents user intention for conversation turn. Each agent has its intents. Entities are parameters of intent that Dialogflow extracts from user expressions. A context in Dialogflow similar to natural language context and helps to control the flow of conversation. The main concepts of IBM Watson Assistant are intents and entities. Intents represent users’ goals. User inputs analyzed to select the most appropriate intent and choose dialog flow. An entity represents information in intent that is relevant to the user’s goal. Microsoft QnA Maker uses a concept of a knowledge base that is filled with information. Requests from the user are analyzed to extract the most appropriate information from the knowledge base. LUIS uses concepts of intent and entities. LUIS uses schemas to extract intent or entity from an input. Important for the choice are the tasks to be solved by the created question-answer system and the volume of system use. Each service has restrictions on the languages that are supported. Dialogflow supports 19 languages, IBM Watson Assistant only 10, Microsoft QnA Maker 52 languages, but LUIS only 17. All services support English, but only Dialogflow and Microsoft QnA Maker support Ukrainian and Russian. .Each service has standard integrations with messengers. All services support Slack and Facebook Messenger. The rest have different support from different services. All services provide options to create a custom integration for non-standard systems if the service does not provide them, through the appropriate APIs. Prices depend on the number of requests per month with a possible limit on the number per minute. Each service offers a free plan under certain conditions, but Microsoft services will require payment for Azure capacity.ukпитально-відповідальні системиDialogflowMicrosoft Bot ServiceMicrosoft QnA MakerLUISIBM Watson Assistantстаттяquestion-answering systemsDialogflowMicrosoft Bot ServiceMicrosoft QnA MakerLUISIBM Watson AssistantArticle