This paper introduces a decomposable attention model for natural language inference (NLI). The model uses attention to decompose the problem into subproblems that can be solved separately, making it trivially parallelizable. On the Stanford Natural Language Inference (SNLI) dataset, the model achieves state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information. Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements. Natural language inference involves determining entailment and contradiction relationships between a premise and a hypothesis. The SNLI dataset, consisting of 570K sentence pairs, is used for this task. The proposed model is compared with prior work and shows superior performance. The model's approach is based on aligning local text substructures and aggregating this information. It uses neural attention to create a soft alignment matrix, which decomposes the task into subproblems that are solved separately. The results of these subproblems are then merged to produce the final classification. Intra-sentence attention is optionally applied to enhance the model's ability to encode substructures. The model's computational complexity is analyzed and shown to be comparable to LSTM-based approaches, but with the advantage of being parallelizable across sentence length. Empirical results on the SNLI corpus show that the model achieves state-of-the-art results with significantly fewer parameters. The model is implemented using TensorFlow and uses 300-dimensional GloVe embeddings. It is trained on the SNLI dataset and achieves high accuracy on the development set. The model outperforms existing methods, particularly in handling neutral cases, while showing some limitations in contradiction cases. The model's performance is further validated through example comparisons with other approaches, demonstrating its effectiveness in various scenarios. The model's approach highlights the importance of pairwise comparisons over global sentence-level representations for NLI tasks.This paper introduces a decomposable attention model for natural language inference (NLI). The model uses attention to decompose the problem into subproblems that can be solved separately, making it trivially parallelizable. On the Stanford Natural Language Inference (SNLI) dataset, the model achieves state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information. Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements. Natural language inference involves determining entailment and contradiction relationships between a premise and a hypothesis. The SNLI dataset, consisting of 570K sentence pairs, is used for this task. The proposed model is compared with prior work and shows superior performance. The model's approach is based on aligning local text substructures and aggregating this information. It uses neural attention to create a soft alignment matrix, which decomposes the task into subproblems that are solved separately. The results of these subproblems are then merged to produce the final classification. Intra-sentence attention is optionally applied to enhance the model's ability to encode substructures. The model's computational complexity is analyzed and shown to be comparable to LSTM-based approaches, but with the advantage of being parallelizable across sentence length. Empirical results on the SNLI corpus show that the model achieves state-of-the-art results with significantly fewer parameters. The model is implemented using TensorFlow and uses 300-dimensional GloVe embeddings. It is trained on the SNLI dataset and achieves high accuracy on the development set. The model outperforms existing methods, particularly in handling neutral cases, while showing some limitations in contradiction cases. The model's performance is further validated through example comparisons with other approaches, demonstrating its effectiveness in various scenarios. The model's approach highlights the importance of pairwise comparisons over global sentence-level representations for NLI tasks.