Query Disambiguation Using Slot Filling and Candidate Replacement
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Solution Overview
Problem
Conventional data analytics chatbots struggle to accurately respond to ambiguous queries due to the challenge of training machine learning models for semantic understanding, leading to inefficient multiple rounds of questions and answers to resolve ambiguities.
Innovation Solution
Implement a slot-filling algorithm that identifies and replaces ambiguous elements in queries with a set of candidate elements based on ambiguity categories and distances, generating directly stated options to resolve ambiguities without extensive machine learning training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained to process user queries, then the chatbot can respond to queries, but the model training becomes computationally expensive and challenging to achieve semantic understanding
Solution Approach 1:
The patent extracts the ambiguous elements from the query and separates them from the rest of the query structure. By identifying and isolating ambiguous words or phrases, the system can process only the relevant portions through the slot-filling algorithm rather than processing the entire query through complex machine learning models, thereby reducing computational cost while maintaining response accuracy.
Solution Approach 2:
The patent segments the query into distinct components: unambiguous parts and ambiguous parts. The slot-filling algorithm then processes only the ambiguous segments by replacing them with candidate elements from predefined slots, while the unambiguous parts are processed directly. This segmentation approach reduces the computational burden compared to processing the entire query through deep machine learning models.
2Reliability
If follow-up questions are asked to resolve ambiguity, then the query can be clarified, but multiple rounds of questions and answers become inefficient
Solution Approach 1:
The patent performs preliminary action by pre-defining slots and their associated candidate elements before the user asks a query. When ambiguity is detected, the system immediately replaces ambiguous elements with relevant candidates from the pre-defined slots without needing to wait for user clarification. This preliminary preparation eliminates the need for multiple rounds of follow-up questions, significantly reducing interaction time while maintaining high disambiguation accuracy.
Solution Approach 2:
The patent creates copies of the query with different interpretations by replacing ambiguous elements with multiple candidate elements from predefined slots. Instead of asking the user to clarify one ambiguity at a time through follow-up questions, the system generates multiple possible query interpretations simultaneously, allowing the user to see and select from multiple options in a single interaction, thereby reducing time loss.
3Adaptability or versatility
If all elements in the database are considered for replacement, then comprehensive coverage is achieved, but the set of potential replacement elements becomes too large
Solution Approach 1:
The patent applies local quality by creating specific slots for different types of ambiguous elements (e.g., temporal expressions, spatial expressions, entity references) and populating each slot with relevant candidate elements. Instead of considering all elements in the database uniformly, the system applies different candidate sets to different local positions in the query based on their semantic role, thereby reducing processing complexity while maintaining comprehensive coverage through targeted candidate generation.
Solution Approach 2:
The patent changes the parameter of candidate element selection from considering all database elements to considering only elements that match specific slot criteria. By transforming the search space from the entire database to filtered candidate sets based on slot definitions and semantic relationships, the system reduces processing complexity while maintaining adaptability through the slot-filling mechanism that can handle various ambiguity types.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for query disambiguation include obtaining a query including an ambiguous element, where the ambiguous element corresponds to an ambiguity category, and selecting a plurality of candidate elements by retrieving the plurality of candidate elements based on the ambiguity category and computing a distance between the ambiguous element and each of the plurality of candidate elements. Some embodiments include generating a plurality of modified queries based on the query by replacing the ambiguous element from the query with each of the plurality of candidate elements, respectively.