Research on Text Similarity Algorithms Based on Interactive Attention

This paper proposes an enhanced text matching model with augmented recurrent attention that utilizes interactive attention mechanisms. During vector encoding, the proposed model employs attention to interact between two input texts. Following the interaction, it leverages Bi-LSTM to re-encode the sequence at a more advanced level, enabling the model to comprehensively learn global information. Additionally, an attention mechanism is incorporated to emphasize the importance of high-weights words. Furthermore, a fusion layer is added to better integrate the two text segments into a single result, which facilitates subsequent text similarity computations. The model demonstrates a high accuracy in text similarity calculations.