tgargiani/Adaptive-Boundary
Metric Learning and Adaptive Boundary for Out-of-Domain Detection (NLDB 2022)
This project helps improve how accurately chatbots, virtual assistants, or other natural language understanding systems identify user requests that fall outside of their known capabilities. By better distinguishing between common queries and truly novel or irrelevant ones, the system can provide more helpful responses or correctly escalate unusual requests. This is useful for anyone managing or deploying conversational AI systems, ensuring they don't misinterpret out-of-scope user input.
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Use this if you need to improve the reliability of your natural language understanding (NLU) systems by more effectively flagging user inputs that are 'out-of-domain' – meaning they don't fit any of your predefined intents.
Not ideal if your primary goal is to categorize known user intents with higher accuracy, rather than specifically identifying unknown or out-of-scope requests.
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Python
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Last pushed
Nov 18, 2022
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