BMIRTE: Design of a Bioinspired Model for Improving Readability of Translated Sentences via Ensemble Operations
Keywords:
Readability, Bioinspired, Corpus, Accuracy, Precision, Recall, Delay, Ensemble, OperationsAbstract
Sentence readability is determined via multiple metrics that include, Flesch Reading Ease, Fog Scale, Flesch-Kincaid Grade Levels, Smog Index, Coleman-Liau Index, Automated Readability Index, Dale-Chall Readability Score, Linear Write Formula, and their consensus. But individual use of these models results in uncertain sentence structures, which limits their usability levels. Moreover, scanning through every combination of these techniques to generate fused readability models is impractical and highly complex under real-time scenarios. To overcome these limitations, this text proposes design of a novel Bioinspired Model for Improving Readability of Translated Sentences via Ensemble Operations. The proposed model initially collects a set of translated texts, and applies stochastic ensemble readability testing via Genetic Algorithm (GA) based process. Due to use of stochastic modelling, the proposed optimizer is capable of identifying corpus specific readability evaluation techniques, that can be used to improve overall readability of multiple sentence types. To perform this task, a readability-based fitness function was evaluated, which assisted in identification of optimum ensemble operations. The model also tracks iterative performance levels of different ensemble combinations, which assists in incrementally improving real-time readability performance for different corpus types.The proposed model was evaluated on multiple translated corpuses, and it was observed that the proposed model outperformed various state-of-the-art methods in terms of readability accuracy, precision, recall, computational delay and memory requirement metrics. Due to which, it was observed to be capable of deployment for a wide variety of real-time post-processing scenarios for translated-texts.
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