"A Comprehensive Framework for Semantic Similarity Detection Using Transformer Architectures and Enhanced Ensemble Techniques"

Transformers and ensembles join forces to redefine semantic similarity detection.

This paper introduces a novel framework for semantic similarity detection. It combines transformer architectures with ensemble techniques to achieve state-of-the-art performance.

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Paper - https://arxiv.org/abs/2501.14288

Original Problem 🧐:

→ Semantic similarity detection is critical for patent search.

→ Existing methods need improvement in accuracy and robustness.

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Solution in this Paper 💡:

→ This paper uses DeBERTa-v3-large as the primary feature extractor.

→ A Bidirectional LSTM layer enhances feature representation by capturing sequential dependencies.

→ Linear attention pooling reduces dimensionality and focuses on key features.

→ Adversarial Weight Perturbation is used to improve robustness.

→ Dynamic target shuffling enhances generalization.

→ Ensemble methods combine diverse models for superior performance.

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Key Insights from this Paper 🧠:

→ Transformer architectures are highly effective for semantic similarity tasks.

→ Bi-LSTM layers improve the model's ability to capture sequential context.

→ Linear attention pooling effectively focuses on important features.

→ Ensemble strategies significantly boost performance and robustness.

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Results 📈:

→ DeBERTa-v3-large achieves 86.1% Pearson correlation.

→ Ensemble Model achieves 87.5% Pearson correlation.

→ Ensemble Model achieves 94.7% AUC.

→ MSE is reduced to 0.011 with the Ensemble Model.