Dialogue Intent Analyzer for Diverse Contextual Responses
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Solution Overview
Problem
Automated response systems using machine learning models have limited trainable parameters, resulting in generic and non-diverse answers, and fail to understand the context of ongoing dialogues, leading to inaccurate responses.
Innovation Solution
A dialogue intent analyzer that predicts answers by identifying intents in questions and responses, using a Seq2Seq natural language processing model with a conversation encoder, question encoder, and decoder, which considers previous conversations to generate more unique, diverse, and contextually relevant responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If machine learning models are used with limited trainable parameters, then the system complexity is reduced, but the diversity and uniqueness of answers decrease
Solution Approach 1:
The model is segmented into distinct encoder components (conversation encoder and question encoder) and a decoder, where each segment has specialized trainable parameters. This segmentation allows different parts of the system to learn different aspects of the task independently, increasing answer diversity without requiring a monolithic complex model.
Solution Approach 2:
The patent introduces intent classification as an additional dimension to the answer generation process. By classifying user intents and generating answers based on intent categories rather than directly from raw input, the system expands the parameter space and creates more diverse answer pathways without significantly increasing overall model complexity.
2Ease of manufacture
If traditional machine learning models are used, then the training process is simpler, but the understanding of dialogue context is insufficient
Solution Approach 1:
The conversation encoder processes and encodes the entire conversation history before the answer generation stage. This preliminary encoding of contextual information ensures that when answers are generated, the full context is already available and structured, improving context understanding without complicating the training process.
Solution Approach 2:
The patent introduces intent classification as an intermediary step between understanding user input and generating answers. This intermediary layer explicitly models the semantic meaning of user intents and uses it to guide answer generation, thereby reducing information loss about dialogue context while maintaining training simplicity.
3Productivity
If generic answer templates are used, then the response time is faster, but the relevance to specific user questions decreases
Solution Approach 1:
The decoder generates answers with local quality by producing different content for different parts of the answer based on the specific user question and identified intent. Rather than using uniform generic templates, each segment of the answer is tailored to the specific context, improving relevance while maintaining efficient generation through the structured decoding process.
Data Source
AI summary
A dialogue intent analyzer uses a conversation between a user and agent, and intents in the conversation to predict a set of answers that better respond to pending user question. The dialogue intent analyzer understands the context surrounding the pending question by capturing and modeling prior conversation and intents within the conversation. Dialogue intent analyzer also reduces genericness in predicted answers by weighting previously used answers based on their frequency and length. Dialogue intent analyzer also increases diversity of predicted answers by using a diverse beam search.