Machine Reading Comprehension Model Based on Fusion of Mixed Attention
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Key Points
The proposed model enhances semantic fusion, resulting in increased performance metrics.
Improvements in BLEU-4 and ROUGE-L scores were noted, surpassing existing models.
Assessment using hybrid attention mechanisms and Bi-LSTM reveals significant results on the DuReader dataset.
The results highlight the need for improved model design to boost reading comprehension in machine learning applications.
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