Multitask Profiler for Customer Query Classification
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Solution Overview
Problem
Existing customer query classification systems face challenges in segregating queries into respective domains efficiently due to the paucity of labeled data and irrelevant cluster formation, leading to high human effort and reduced classification accuracy.
Innovation Solution
A system and method utilizing a multitask profiler subsystem that includes a tokenizer to split queries into tokens, a mapping module to assign mathematical codes using pre-trained embedding data, an attention module to apply hierarchical attention models for contextual coding, and a classification module to categorize queries into profiles, generating both human-readable and machine-readable profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human assisted categorization is used to handle customer queries, then classification accuracy can be maintained, but the time and effort required increases significantly
Solution Approach 1:
The customer query is segmented into multiple tokens using a tokenizer subsystem, which then processes each token independently through embedding mapping and attention mechanisms. This segmentation allows parallel processing of query components, reducing overall processing time while maintaining classification accuracy through cumulative analysis of token-level features.
Solution Approach 2:
A multitask profiler subsystem acts as an intermediary between the raw customer query and the final classification output. This subsystem incorporates domain expert knowledge through pre-trained embedding data and applies hierarchical attention models to bridge the gap between unstructured query text and structured classification profiles, automating the categorization process without requiring human intervention.
2Measurement precision
If supervised methods are used for text categorization, then classification accuracy improves, but the requirement for large amounts of training data increases
Solution Approach 1:
Domain expert knowledge is incorporated in advance through pre-trained embedding data that maps tokens to mathematical codes before the actual classification task. This preliminary encoding of domain knowledge allows the system to perform accurate classification with minimal task-specific training data, as the embedding layer already contains learned representations of domain concepts.
Solution Approach 2:
The system transforms the classification problem from requiring large labeled datasets to utilizing pre-trained embedding parameters. By changing the approach from learning representations from scratch to leveraging pre-trained embeddings with hierarchical attention, the system achieves high classification accuracy with paucity of labeled data.
3Quantity of substance
If unsupervised methods are used for text categorization, then the requirement for labeled data decreases, but irrelevant cluster formation occurs reducing accuracy
Solution Approach 1:
The hierarchical attention model acts as an intermediary that guides the clustering process by weighting the importance of different tokens and their relationships. This attention mechanism prevents irrelevant cluster formation by focusing on semantically important tokens while downweighting noise, thereby maintaining classification accuracy without requiring labeled data for training the attention weights.
Solution Approach 2:
The system applies different processing quality to different parts of the query by using hierarchical attention. Important tokens receive higher attention weights and more rigorous processing, while less important tokens receive lower weights. This local differentiation in processing quality ensures that classification decisions are based on relevant features, preventing irrelevant clustering.
Data Source
AI summary
A system for classification of a customer query is disclosed. The system includes a customer interaction subsystem to receive the customer query from a customer, and a tokenizer subsystem to split the customer query into tokens. The system also includes a multitask profiler subsystem including a mapping module to map the tokens with pre-trained embedding data to assign mathematical codes to the tokens, an attention module to apply attention models hierarchically on a contextual embedding layer to obtain contextual mathematical codes corresponding to the tokens based on the mathematical codes, a classification module to classify the multiple tokens into profiles based on the contextual mathematical codes, and a profile generator to generate a human readable profile and a machine-readable profile based on the profiles. The machine-readable profile and the human readable profile includes at least one of a customer profile, a product profile, an issue profile or a combination thereof.