AI Text Optimization via Planning Operators
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Solution Overview
Problem
Current natural language processing technologies face challenges in optimizing translated texts by removing redundant information, reordering sentences for better structure, and paraphrasing, which affects the quality and clarity of translations.
Innovation Solution
The approach involves a text optimization module that applies planning operators such as swapping, adding, or removing sentences, and paraphrasing, using a heuristic search and iterative deepening depth-first search to improve the score of the translated text based on a scoring function, ultimately producing an optimized text in the target language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If planning operators are applied to optimize translated text, then translation quality and clarity are improved, but computational complexity and processing time increase
Solution Approach 1:
The text optimization process is divided into discrete planning operators (swapping, adding, removing, paraphrasing sentences) that can be independently applied and evaluated. Each operator represents a modular unit of transformation that can be systematically explored through heuristic search, allowing the complex optimization problem to be broken down into manageable segments.
Solution Approach 2:
The system dynamically adjusts the text structure by applying planning operators based on scoring function evaluations. The optimization process is iterative and adaptive, where the sequence and selection of operators change dynamically as the search progresses through the solution space, allowing the system to respond to feedback from the scoring function.
2Manufacturing precision
If heuristic search with iterative deepening depth-first search is used to apply planning operators, then text optimization quality improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary evaluations using the scoring function to assess potential transformations before fully committing to them. The iterative deepening depth-first search explores the solution space systematically, evaluating candidate solutions in advance and pruning unpromising branches early, which reduces the overall computational burden despite the thoroughness of the search.
Solution Approach 2:
The scoring function provides continuous feedback during the heuristic search process, guiding the selection and application of planning operators. This feedback mechanism allows the system to learn from each evaluation and adjust its search strategy, improving optimization quality over time while managing computational resources more efficiently through informed decision-making.
Data Source
AI summary
In an approach for discourse-level text optimization, a processor receives an initial text in a first language. A processor applies one or more operators to modify the initial text. A processor evaluates the modified text using a scoring function. A processor determines whether a score generated from the scoring function on the modified text is above a predefined threshold. In response to determining the score is above the predefined threshold, a processor outputs the modified text.