Chatbot Contextual Feedback for Natural Language Understanding
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Solution Overview
Problem
Natural language interfaces, such as chatbots, face challenges in understanding ambiguous user inputs due to the lack of contextual information, leading to inaccurate responses as they rely solely on the current utterance without considering the shared context from previous conversations.
Innovation Solution
A chatbot computing system that includes a natural language processor and a bot controller, where the natural language processor identifies concepts in user inputs and feeds back context information from previous utterances to enhance the understanding of subsequent inputs, using a knowledge model and context filter/enhancement logic to manage and update contextual relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the natural language processor relies solely on the current utterance without contextual information, then the system complexity remains low, but the response accuracy deteriorates due to ambiguity in user inputs
Solution Approach 1:
The system performs preliminary action by identifying and storing concepts from previous utterances before processing the current input. The natural language processor maintains a context window that captures concepts from prior conversations, making this contextual information available for disambiguation before the current utterance is fully processed. This preliminary capture of contextual data allows the system to resolve ambiguities more accurately without requiring complex real-time analysis.
Solution Approach 2:
The system implements feedback by feeding the output of concept identification from previous utterances back into the natural language processor as contextual information for subsequent inputs. This creates a closed-loop system where the conceptual understanding from prior interactions continuously informs and refines the interpretation of current user inputs, progressively improving response accuracy while maintaining a manageable system architecture.
2Reliability
If the system processes only the current textual input without feedback from previous concepts, then the processing time is short, but the understanding of ambiguous queries deteriorates
Solution Approach 1:
The system applies partial action by selectively processing only the most recent utterances within a fixed context window rather than analyzing the entire conversation history. This approach provides sufficient contextual information to resolve ambiguities in most practical scenarios while avoiding the excessive processing time that would result from analyzing complete conversation histories. The context window size is optimized to balance understanding accuracy with processing efficiency.
3Adaptability or versatility
If context information from previous utterances is incorporated into subsequent processing, then the contextual awareness improves, but the computational complexity increases
Solution Approach 1:
The system extracts only the essential conceptual information from previous utterances and stores it in a structured format within the context window. Rather than processing entire previous messages or maintaining complex conversation states, the system extracts key concepts and their relationships, creating a condensed representation of contextual information that enhances adaptability while minimizing computational overhead.
Data Source
AI summary
A chat bot computing system includes a bot controller and a natural language processor. The natural language processor receives a first textual input and identifies concepts represented by the first textual input. An indication of the concepts is output to the bot controller which generates a response to the first textual input. The concepts output by the natural language processor are also fed back into the input to the natural language processor, as context information, when a second textual input is received. The natural language processor then identifies concepts represented in the second textual input, based on the second natural language, textual input and the context information.