Transformer, a type of neural network architecture, have witnessed a surge in popularity in recent years due to their outstanding performance in natural language processing tasks. Distinct from traditional recurrent neural networks, transformers eschew sequential processing of input data. This unique feature empowers them to capture long-term dependencies within the input sequence with far greater efficiency.
The core of transformers lies in their self-attention mechanism. This mechanism, known as the "attention mechanism," enables transformers to assign weights to different input elements when generating output predictions. It calculates the relevance of each element in the input sequence to the current output prediction and assigns a corresponding weight. Thanks to this mechanism, transformers can effectively capture contextual information, rendering them highly valuable in language-related tasks.
Machine translation stands as one of the most renowned applications of transformers. In this task, a transformer takes a sentence in one language as input and generates a corresponding sentence in another language as output. By leveraging the attention mechanism, the transformer can precisely identify and prioritize the most relevant parts of the input sentence, thus producing an accurate output translation.
Beyond machine translation, transformers have also attained state-of-the-art results in various other natural language processing tasks, such as language modeling and question-answering. Their proficiency in grasping contextual information and long-term dependencies in input data makes them especially effective in tasks that require in-depth analysis of text sequences.