Variable Slot Identification and Replacement in Conversational Agents
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Solution Overview
Problem
Traditional natural language understanding (NLU) techniques struggle to accurately update slots in conversational agents, particularly with multi-value and numeric slots, leading to incorrect dialogue state management due to the inability to track context and perform precise updates across conversational turns.
Innovation Solution
The system identifies and replaces single-value slots with variable slots, capable of holding multiple values or adjustable values, using rule-based modifications to update slot values based on context, allowing for accurate tracking and management of dialogue states by determining the type of update (e.g., appending, removing, increasing, decreasing, or replacing) based on the intent and context of utterances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional NLU techniques use a replace function to update slots, then the update process is simple, but the accuracy of dialogue state management deteriorates for multi-value and numeric slots
Solution Approach 1:
The patent introduces dynamic update strategies that adapt based on slot type. Instead of a static replace function, the system dynamically selects between replace, append, or increment operations based on whether the slot is multi-value, numeric, or single-value, thereby maintaining both simplicity and accuracy.
Solution Approach 2:
The patent changes the parameter of the update operation based on slot characteristics. By detecting the slot type (multi-value, numeric, single-value) and adjusting the update strategy accordingly, the system achieves accurate dialogue state management without sacrificing operational simplicity.
2Device complexity
If traditional NLU techniques use a replace function to update slots, then the implementation is straightforward, but the ability to track context across conversational turns deteriorates
Solution Approach 1:
The system dynamically adjusts the update mechanism based on context requirements. For slots that need to accumulate information across turns (multi-value slots), the system dynamically switches from replace to append operations, preserving context while maintaining implementation simplicity.
Solution Approach 2:
The patent changes the update parameter based on slot type and context. By identifying whether a slot should accumulate values or be replaced, the system maintains straightforward implementation while preventing information loss through context-aware update strategies.
3Measurement precision
If variable slots are introduced to handle multiple values and adjustable values, then the accuracy of slot updates improves, but the system complexity increases
Solution Approach 1:
The patent segments slots into distinct categories (multi-value slots, numeric slots, single-value slots) with specific update rules for each. This segmentation allows the system to handle complex update scenarios accurately while keeping the implementation organized and manageable through clear categorization.
Solution Approach 2:
The system changes the update parameter based on detected slot type. By automatically identifying whether a slot is multi-value, numeric, or single-value and applying the appropriate update strategy, the system achieves high accuracy without requiring complex manual configuration or processing logic.
4Measurement precision
If rule-based modifications are implemented to update slots based on context and intent, then the accuracy of dialogue state management improves, but the processing complexity increases
Solution Approach 1:
The patent changes the update parameter based on detected slot type and context. By automatically identifying whether a slot is multi-value, numeric, or single-value and applying the appropriate update strategy (append, increment, or replace), the system achieves high accuracy while keeping processing logic streamlined through automated detection rather than manual rule configuration.
Data Source
AI summary
Methods, programming, and system for identifying one or more variable slots within an utterance are described herein. In a non-limiting embodiment, a first slot-value pair for an utterance may be obtained. The first slot-value pair may include a first slot and a first value associated with the slot. The first slot may be of a first entity type, where an intent and a data object are estimated based on the first utterance. A data structure representing the data object may be identified. Based on the intent, a first variable slot in the data structure associated with the first entity type may be determined, where the first variable slot may be associated with at least one of: multiple values and an adjustable value. Based on the intent, the first value may be assigned to the first variable slot in the data structure.