Conversational System Intent Network for Multi-Intent Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conversational systems struggle to accurately interpret human intentions, as they lack the ability to recognize and act on multiple explicit and implicit intents, leading to disconnected and poorly interpreted human-computer interactions.
Innovation Solution
A system and method that utilizes machine learning algorithms, semantic parsing, and rule-based algorithms to process utterances, infer relationships among intents, and generate responses based on unstated intentions, creating a knowledge graph to prioritize and understand explicit and implicit intents, enabling more natural and human-like dialogue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conversational systems operate on explicit intent recognition with simple phrasing assumptions, then system complexity is reduced, but interpretation accuracy deteriorates
Solution Approach 1:
The system segments intent recognition into multiple layers: explicit intent extraction from utterances, implicit intent inference from context and relationships, and hypothesis generation for alternative interpretations. This segmentation allows the system to handle complex interpretation tasks through modular processing stages, maintaining manageable system complexity while improving interpretation accuracy through progressive refinement.
Solution Approach 2:
The system introduces an intermediary intent network that acts as a mediator between raw utterances and final interpretations. This intent network extracts, infers, and prioritizes multiple intents before generating responses, serving as a buffer that enhances interpretation accuracy without directly increasing the complexity of the core conversational system.
2Measurement precision
If conversational systems recognize and act on multiple explicit and implicit intents, then interpretation accuracy is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by extracting and inferring multiple intents from utterances before generating final responses. The intent network pre-processes input by identifying explicit intents, inferring implicit intents through relationship analysis, and prioritizing them in advance. This preliminary processing improves interpretation accuracy while organizing complexity into manageable pre-computation steps.
Solution Approach 2:
The system adds another dimension to intent recognition by operating in the semantic space of relationships between intents, not just individual utterances. The intent network analyzes relationships among multiple intents simultaneously, inferring implicit intents based on how they relate to each other. This dimensional expansion improves interpretation accuracy while the modular architecture keeps system complexity manageable.
3Ease of operation
If human discourse is interpreted with natural ambiguity and fluidity, then conversational naturalness is improved, but measurement precision deteriorates
Solution Approach 1:
The system dynamically adapts its interpretation strategy based on the conversational context. The intent network adjusts which intents to prioritize and how to infer implicit meanings based on the flow of conversation, allowing natural handling of ambiguous and fluid human discourse. This dynamic approach maintains interpretation accuracy while improving conversational naturalness by flexibly responding to varying discourse conditions.
Solution Approach 2:
The system incorporates feedback mechanisms where the intent network continuously refines intent interpretations based on conversational context and relationships. By analyzing how intents relate to each other across multiple utterances, the system can disambiguate ambiguous inputs and maintain accurate interpretation even as discourse becomes more fluid and natural. This feedback-driven refinement preserves precision while accommodating natural conversational variability.
Data Source
AI summary
A conversational system that recognizes, understands, and acts on multiple intents that may be explicit or implicit during conversations with humans. During a conversation, one or more utterances are received and processed through a plurality of machine learning algorithms to establish precise meanings, additional intentions, and alternative hypothesis. Using a combination of machine learning algorithms and datastores, conversations are interpreted as intended and may diverge where needed or desired, delivering a more useful, natural, and human-like dialogue between machines and people.