Intent Resolution System Using Vector Concatenation for Digital Assistants
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Solution Overview
Problem
Current conversational interfaces, such as digital assistants, often fail to accurately understand user intentions due to unknown words, paraphrased language, long utterances, or the absence of verbs, leading to misinterpretation of user queries.
Innovation Solution
An intent resolution system that generates two vectors for user utterances using TF-IDF and GloVe models, combines them, and employs a machine learning model to classify user intent, enabling the system to handle unknown words, paraphrased language, and complex utterances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword matching is used for intent recognition, then the system is simple to implement, but it fails to understand paraphrased language and unknown words
Solution Approach 1:
The patent introduces vector representations as an intermediary between user utterances and intent classification. Instead of direct keyword matching, the system converts utterances into vector embeddings that capture semantic meaning, enabling the recognition of paraphrased language and unknown words while maintaining a manageable system architecture through the use of pre-trained language models
Solution Approach 2:
The system changes the parameter space from discrete keywords to continuous vector representations. By transforming textual utterances into continuous vector embeddings and using similarity-based classification, the system achieves better generalization to unseen words and paraphrases while controlling complexity through dimensionality reduction techniques
2Adaptability or versatility
If the system uses a large vocabulary to cover all possible user inputs, then intent recognition accuracy improves, but the system becomes harder to maintain and less adaptable
Solution Approach 1:
The patent employs universal vector embeddings that can represent any word or phrase, including unknown words and paraphrases, without requiring explicit vocabulary entries. This multi-functional representation system handles diverse inputs (known words, unknown words, paraphrases, and complex sentences) through a unified vector space, eliminating the need for separate vocabulary management for different input types
Solution Approach 2:
The system uses pre-trained language model embeddings that capture semantic relationships without requiring the system to explicitly store or manage a comprehensive vocabulary. The vector representations copy semantic meaning from training data, enabling the system to generalize to unseen words and phrases while keeping the actual vocabulary storage minimal
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for training a machine learning model and using the machine learning model to determine an intent associated with an utterance. An embodiment operates by receiving an utterance, determining a first vector based on the received utterance, and determining a second vector based on the received utterance. A third vector is generated by concatenating the first vector and the second vector. The third vector is used to perform at least one of classifying, using a machine learning model, the utterance to determine the intent associated with the utterance or training the machine-learning model.