Answer Rephrasing Model for Talking Style Adaptation
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Solution Overview
Problem
Current systems fail to provide answers to questions in a form that matches the talking style of the questioner, leading to confusion and misinformation due to the need for individuals to sift through varied and complex search results.
Innovation Solution
A computer-implemented system that analyzes the talking style of entities and employs neural network models to rephrase standard answers into formats that are easily understandable, using parallel corpus data and sequence-to-sequence recurrent neural networks to generate tailored responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard answers are provided to questions, then information accuracy is maintained, but understanding and comprehension by diverse users deteriorates
Solution Approach 1:
The system dynamically adapts the answer format based on the questioner's talking style. Multiple answer rephrasing models are trained on different talking styles (e.g., formal, informal, technical, layman terms), and the appropriate model is selected based on classification of the input question's style. This allows the same accurate information to be presented in multiple formats suitable for different user comprehension levels.
Solution Approach 2:
The system changes linguistic parameters of the answer based on the detected talking style. This includes adjusting vocabulary complexity, sentence structure, tone, and technicality level while preserving the core accurate information. The answer rephrasing models transform standard answers into customized versions that match the questioner's preferred communication style.
2Loss of information
If multiple search results are provided to ensure comprehensive information, then information completeness is improved, but user confusion and time to find accurate information increases
Solution Approach 1:
The system segments the information delivery process into distinct stages: classification of talking style, selection of appropriate rephrasing model, and generation of customized answer. This segmentation allows the system to process and deliver comprehensive information in a structured, efficient manner rather than presenting all possible answers simultaneously.
Solution Approach 2:
The answer rephrasing models act as intermediaries between the standard accurate answer and the user. Instead of presenting multiple raw search results, the system uses these intermediary models to transform the standard answer into multiple customized versions, each tailored to different user preferences, thereby reducing confusion while maintaining information completeness.
3Measurement precision
If answer rephrasing models are trained on parallel corpus data, then talking style accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary training of multiple answer rephrasing models on parallel corpus data during the system setup phase. The parallel corpus contains standard answers paired with their rephrased versions in different talking styles. This preliminary action prepares the models in advance, so during operation, the system can quickly classify and select the appropriate model without performing complex training in real-time.
Solution Approach 2:
The system uses parallel corpus data that contains copied and adapted versions of standard answers in different talking styles. During training, the models learn to copy the structural and linguistic patterns from the parallel corpus, enabling them to generate accurate rephrased answers without requiring complex real-time analysis.
4Adaptability or versatility
If classification of talking style is performed for each question, then personalization accuracy is improved, but processing time increases
Solution Approach 1:
The classification component is designed to be a universal, reusable module that can quickly identify talking styles across different questions. Once trained on diverse data, it can handle various types of questions efficiently. The system reuses the same classification model for all inputs, avoiding the need for question-specific training while maintaining high personalization accuracy.
Data Source
AI summary
Techniques are described that facilitate automatically providing entities with rephrased versions of standard answers. In one embodiment, a computer-implemented is provided that comprises determining, by a device operatively coupled to a processor, a talking style of a plurality of talking styles that an entity is associated with based on reception of natural language input from the entity proposing a question related to a defined topic. The method further comprises selecting, by the device based on the talking style, an answer rephrasing model from a plurality of answer rephrasing models respectively configured to generate different rephrased versions of a standard answer to the question, and employing, by the device, the answer rephrasing model to generate a rephrased version of the standard that corresponds to the talking style.