The paper introduces a novel neural architecture for natural language inference (NLI) that leverages attention mechanisms to decompose the problem into smaller, parallelizable subproblems. This approach significantly reduces the number of parameters required compared to previous methods while achieving state-of-the-art results on the Stanford Natural Language Inference (SNLI) dataset. The model first creates a soft alignment matrix using neural attention, then compares aligned subphrases separately, and finally aggregates the results to produce the final classification. The authors also introduce intra-sentence attention to enhance the model's ability to capture compositional relationships within sentences. The computational complexity of the proposed model is analyzed and shown to be comparable to or better than LSTM-based approaches, making it highly efficient and parallelizable. Experimental results demonstrate that the model outperforms complex neural architectures with fewer parameters, highlighting the importance of pairwise comparisons in NLI tasks.The paper introduces a novel neural architecture for natural language inference (NLI) that leverages attention mechanisms to decompose the problem into smaller, parallelizable subproblems. This approach significantly reduces the number of parameters required compared to previous methods while achieving state-of-the-art results on the Stanford Natural Language Inference (SNLI) dataset. The model first creates a soft alignment matrix using neural attention, then compares aligned subphrases separately, and finally aggregates the results to produce the final classification. The authors also introduce intra-sentence attention to enhance the model's ability to capture compositional relationships within sentences. The computational complexity of the proposed model is analyzed and shown to be comparable to or better than LSTM-based approaches, making it highly efficient and parallelizable. Experimental results demonstrate that the model outperforms complex neural architectures with fewer parameters, highlighting the importance of pairwise comparisons in NLI tasks.