The difference between the knowledge question and answer session with the knowledge

Knowledge question answering is similar to a category of knowledge conversation, closed domain conversation; knowledge conversation has both closed domain conversation and open domain conversation, so knowledge question answering can be understood as a branch or subset of knowledge conversation technology.
The original knowledge question answering system based on template matching and semantic analysis mainly relies on knowledge graph and deep learning technology that has developed rapidly in recent years.These technologies have greatly improved the question-answering system's ability to understand questions and match answers, so that users no longer need to fully grasp the syntax of structured query language to operate and use.However, judging from the current technological development, the question answering system can still only reply a single and accurate answer when the user queries a certain knowledge, and the dialogue continuity will still be poor, and the answer may not be answered.Some types will also have poor scalability and portability.At the same time, the question answering system has 2 roles and 1 interaction feature in use.
However, the data-driven general knowledge conversation system does not have the above problems. A significant difference between it and the question answering system is that it does not need to get an accurate answer.Because general-purpose conversations are not limited to inquiries about knowledge, they can also conduct small talk, which is relatively free to develop without accurate answers.Therefore, the conversational system can technically save the process of language understanding compared with the question answering system.Not only that, compared to question answering systems, conversational systems can act between 2 or more characters at the same time, and can have multiple interactions.
At present, the research on conversation is very diverse. At present, most of them focus on improving the corresponding diversity and giving corresponding knowledge and information. In the future, they can be applied to intelligent chat and entertainment robots; while the answering system that answers relatively accurate but blunt tasks in specific fields cannotAs often as conversational systems are used in human daily life, but should be able to shine in personal professional assistants and other question answering systems.