Retail Conversational NLU With BERT And Dependency Embeddings
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Solution Overview
Problem
Conversational systems, such as voice assistants and chatbots, often misinterpret customer commands due to discrepancies in natural language understanding, particularly in retail domains, leading to inconsistent responses.
Innovation Solution
Employing a machine learning process that utilizes a Bidirectional Encoder Representation from Transformers (BERT) model trained on retail data, combined with dependency-based word embeddings, to enhance natural language understanding tasks like POS tagging, intent detection, and entity recognition, and sentiment analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional NLP systems are used for voice commands, then basic command capture and response generation is achieved, but misinterpretation of domain-specific meanings occurs leading to inaccurate responses
Solution Approach 1:
The system changes the parameter of training data from general domain to retail-specific domain data. By training the BERT model on retail domain texts (product descriptions, customer queries, transaction records), the model learns domain-specific language patterns and terminology, enabling accurate interpretation of retail-related commands while maintaining general NLP capabilities
Solution Approach 2:
The system segments the NLP processing into distinct modules: BERT-based intent classification, dependency-based entity recognition, and domain-specific sentiment analysis. Each module handles specific aspects of NLU independently, allowing specialized processing for domain-specific terminology while maintaining overall system coherence
2Adaptability or versatility
If general domain models are used, then broad applicability is maintained, but domain-specific terminology and context are misinterpreted
Solution Approach 1:
The system applies local quality by using dependency-based word embeddings that provide contextual information specific to each word's role in the sentence. The dependency parser analyzes the grammatical relationships between words, allowing the model to distinguish between homonyms based on their specific contextual usage in retail domains (e.g., interpreting 'add' as either adding items to cart or adding products to inventory based on sentence structure and context)
3Ease of operation
If traditional chatbot interfaces are used, then text communication is enabled, but grammatical inconsistencies and idiosyncrasies cause response discrepancies
Solution Approach 1:
The system implements feedback mechanisms where the BERT model continuously refines its understanding of user input based on contextual clues and domain patterns. The dependency-based entity recognition provides feedback about the structure and intent of commands, allowing the system to adjust its interpretation and generate consistent responses even when users make grammatical errors or use unconventional phrasing
Data Source
AI summary
This application relates to apparatus and methods for natural language understanding in conversational systems using machine learning processes. In some examples, a computing device receives a request that identifies textual data. The computing device applies a natural language model to the textual data to generate first embeddings. In some examples, the natural language model is trained on retail data, such as item descriptions and chat session data. The computing device also applies a dependency based model to the textual data to generate second embeddings. Further, the computing device concatenates the first and second embeddings, and applies an intent and entity classifier to the concatenated embeddings to determine entities, and an intent, for the request. The computing device may generate a response to the request based on the determined intent and entities.