Contextual Biasing Neural Language Models for Chatbot Context Awareness
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Solution Overview
Problem
Conversational interfaces and transcription services face challenges in handling goal-oriented conversations that span multiple turns, as existing methods either require manual creation of explicit grammars or reset context for each utterance, leading to inefficiencies and errors.
Innovation Solution
Implementing a chatbot system that uses contextual neural language models to adapt to recent user interactions through embeddings, allowing self-training and efficient similarity prediction without storing explicit utterances, thereby leveraging implicit biasing for improved context awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If explicit grammars are manually created to model interactions, then the system can handle goal-oriented conversations, but the device complexity and ease of manufacture deteriorate due to rigid structures requiring manual writing for all scenarios
Solution Approach 1:
The patent replaces manual grammar creation with neural language models that automatically learn conversational patterns from data. The system uses trained neural networks to understand and generate natural language responses without requiring explicit grammatical rules to be hand-crafted, thereby reducing device complexity while maintaining adaptability
Solution Approach 2:
The neural language model performs self-training by automatically learning from conversation data and improving its own performance without manual intervention. The system adapts to new conversational patterns through continuous learning, eliminating the need for ongoing manual grammar updates
2Speed
If context is reset for each utterance, then the system achieves faster processing, but the reliability deteriorates due to loss of conversational context leading to errors
Solution Approach 1:
The patent implements dynamic context management where the neural language model maintains conversational context across multiple utterances through its internal state and attention mechanisms. The model can selectively retain or forget information based on relevance, allowing it to preserve necessary context for reliability while managing computational resources efficiently
Solution Approach 2:
The system processes conversations by adding a temporal dimension, where previous utterances are incorporated into the current processing through sequence modeling. This allows the model to consider historical context without significantly increasing processing time for each individual utterance
3Measurement precision
If explicit utterances are stored for training, then the system can improve accuracy, but the loss of substance increases due to computational expense and potential errors from mismatched training examples
Solution Approach 1:
The patent uses embeddings to create compressed numerical representations of utterances that capture their semantic meaning. Instead of storing and processing full text examples, the system works with compact embedding vectors that preserve the essential information needed for accurate predictions while dramatically reducing computational and storage requirements
Data Source
AI summary
Techniques for implementing a chatbot that utilizes context embeddings are described. An exemplary method includes determining a next turn by: applying a language model to the utterance to determine a probability of a sequence of words, generating a context embedding for the utterance based at least on one or more of: a dialog act as defined by a chatbot definition of the chatbot, a topic vector identifying a domain of the chatbot, a previous chatbot response, and one or more slot options; performing neural language model rescoring using the determined probability of a sequence of words as a word embedding and the generated context embedding to predict an hypothesis; determining at least a name of a slot and type to be fulfilled based at least in part on the hypothesis and the chatbot definition; and determining a next turn based at least in part on the chatbot definition, any previous state, and the name of the slot and type to be fulfilled.