Deep Learning-based Sentence Embeddings using BERT for Textual Entailment

This study directly and thoroughly investigates the practicalities of utilizing sentence embeddings, derived from foundations deep learning, for textual entailment recognition, with a specific emphasis on robust BERT model. As cornerstone our research, we incorporated Stanford Natural Language Inference (SNLI) dataset. Our emphasizes meticulous analysis BERT’s variable layers to ascertain optimal layer generating embeddings that can effectively identify entailment. approach deviates traditional methodologies, as base evaluation direct simple comparison norms, subsequently highlighting geometrical attributes embeddings. Experimental results revealed L2 norm drawn specifically 7th layer, emerged superior in detection compared other setups.