Dialog-History Question Generation for Coherent Natural Language Responses
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing natural language processing systems struggle to generate coherent and engaging responses to user inputs, particularly when inputs are short and ambiguous, and lack the ability to utilize dynamic information pools effectively.
Innovation Solution
A system that employs question generators to determine relevant questions based on user inputs and dialog history, filters these questions for relevance, and uses web-based answer generators to produce responses, incorporating cross-attention mechanisms to ensure coherence and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing natural language processing systems are used to generate responses, then the system structure remains simple, but the coherence and engagement of responses deteriorate when inputs are short and ambiguous
Solution Approach 1:
The system segments the response generation process into distinct modules: question generator, answer generator, and response generator. Each module handles a specific aspect of processing user inputs and generating responses, allowing the system to maintain high response coherence through specialized processing while keeping each individual module relatively simple and manageable.
Solution Approach 2:
The patent introduces intermediary components including a question generator that creates intermediate questions from user inputs, and an answer generator that produces intermediate answers. These intermediaries bridge the gap between simple user inputs and coherent responses, enabling the system to generate engaging responses even when direct mapping is difficult.
2Adaptability or versatility
If traditional processing techniques are used, then the processing speed remains fast, but the ability to handle ambiguous inputs deteriorates
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate questions and answers before final response selection. The question generator pre-processes user inputs into structured questions, and the answer generator pre-computes multiple potential answers. This preliminary processing enables the system to efficiently handle ambiguous inputs by having candidate responses ready for quick selection.
Solution Approach 2:
The system employs partial action by generating a subset of relevant questions and answers rather than exhaustively processing all possible interpretations. The cross-attention mechanism selectively attends to the most relevant parts of the input and generated content, processing only what is necessary to produce coherent responses without unnecessary computational overhead.
3Reliability
If static information sources are used, then the system remains simple to implement, but the quality of responses deteriorates
Solution Approach 1:
The system transitions from static to dynamic information sources by implementing a question generator that adapts question generation based on user inputs and dialog history. The answer generator dynamically retrieves and processes information from multiple sources including web searches and knowledge bases, allowing the system to access relevant information on-demand rather than relying on pre-stored static data.
Solution Approach 2:
The system achieves universality by integrating multiple information sources including web searches, knowledge bases, and dialog history into a unified processing framework. The cross-attention mechanism in the response generator can universally process information from any of these sources, allowing the system to leverage diverse information sources while maintaining a cohesive architecture.
Data Source
AI summary
Techniques for generating a natural language response to a user input of a dialog are described. A system receives a natural language user input of a dialog and determines dialog history data including a previous natural language user input of the dialog. Based on the first natural language user input and the dialog history data, the system generates at least a first question associated with the natural language user input. Based on the first natural language user input and the dialog history data, the system generates at least a first answer to the at least first question. Using the dialog history data, the first natural language question, and the first natural language answer, the system generates an output responsive to the natural language user input.