Natural Language Generation Using AI Slot Data
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Solution Overview
Problem
Conventional natural language generation modules in dialogue systems often require multiple templates to provide varied responses, leading to decreased usability due to the potential for generating abnormal or awkward responses, highlighting the need for accurate natural language generation in electronic devices.
Innovation Solution
An electronic device and method that utilize trained artificial intelligence models to generate natural language by inputting data with multiple slots, identifying and using existing templates, or generating responses using similar templates and calculating scores to ensure high-quality output, with defaulted templates used when scores are below a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple natural language generation templates are used to provide varied responses, then the coverage of different situations is improved, but the risk of generating abnormal or awkward responses increases
Solution Approach 1:
The patent implements a feedback mechanism where the determination module evaluates the suitability of each candidate template based on the current dialogue context and user input. The selection module chooses the most appropriate template by comparing multiple candidates, ensuring that the generated response is both varied and high-quality. This feedback-driven selection process resolves the contradiction by dynamically adapting template choice to maintain response reliability while covering diverse situations.
2Device complexity
If conventional natural language generation templates are used, then the system structure is simple, but the ability to provide accurate responses to various cases is limited
Solution Approach 1:
The patent transforms the static template selection process into a dynamic one by introducing determination and selection modules that adaptively choose templates based on real-time analysis of dialogue context and user input. This dynamic approach allows the system to maintain a relatively simple overall structure while achieving high response accuracy through intelligent, context-aware template selection rather than relying on a large number of fixed templates.
Data Source
AI summary
Provided in the present disclosure are an electronic device and a natural language generation method thereof. The natural language generation method of the electronic device comprises: obtaining input data with information on a plurality of slots included for generating a response; obtaining a natural language corresponding to the input data by inputting the information on the plurality of slots to one of artificial intelligence models trained for obtaining a natural language generation template and a natural language; and outputting the obtained natural language. In particular, at least a part of a method for obtaining a natural language in order to provide a response can use an artificial intelligence model having learned according at least one of machine learning, a neural network, or a deep-learning algorithm