Attention Mechanism and Transformer Model
Posted: Thu Feb 06, 2025 3:18 am
Taking machine translation tasks in natural language processing as an example, LSTM can better capture long-distance dependencies in the source language, thereby improving translation quality. For example, when translating a long sentence, LSTM can remember key information at the beginning of the sentence through its gating structure and use this information to generate a more accurate translation when needed.
Since the advent of LSTM, it has achieved remarkable results in various sequence tasks, including natural language processing, speech recognition, video analysis, etc. However, despite its advantages in panama mobile database capturing long-distance dependencies, LSTM still suffers from low computational efficiency. In addition, the performance of LSTM still needs to be improved when processing very long sequences.
To solve these problems, researchers proposed a new model architecture, Transformer. Transformer abandons the loop structure and introduces an attention mechanism to capture dependencies in the sequence. In the next section, we will discuss in detail the development and principles of the attention mechanism and Transformer model.
Since the advent of LSTM, it has achieved remarkable results in various sequence tasks, including natural language processing, speech recognition, video analysis, etc. However, despite its advantages in panama mobile database capturing long-distance dependencies, LSTM still suffers from low computational efficiency. In addition, the performance of LSTM still needs to be improved when processing very long sequences.
To solve these problems, researchers proposed a new model architecture, Transformer. Transformer abandons the loop structure and introduces an attention mechanism to capture dependencies in the sequence. In the next section, we will discuss in detail the development and principles of the attention mechanism and Transformer model.