Context switching in chatbots is similar to the context switching that occurs throughout a conversation between humans. It happens when we speak about one topic and then switch to another mid-conversation. For example, when speaking about the weather with a friend, you might quickly jump to the topic of scheduling plans for the weekend.
Humans are capable of recognizing and responding to these switches very quickly, however chatbots sometimes have difficulty in doing so and oftentimes become confused and unable to respond. In the chatbot industry, this typically relates to deployments that are use case-specific, rendering most queries that differ from projected conversational flows as “out of scope”.
The ability for chatbots to switch between contexts is significant when wanting to provide a user with a fluid conversational experience. Nevertheless, context switching is often overlooked when chatbots are being designed, and only prioritized once the chatbot is live and users begin to notice the rigidity of the dialogue.
Hyro solves this commonly encountered issue of context rigidity by combining knowledge graphs, natural language processing and computational linguistics to transform and refine the entire state of conversational experiences.