Abstract: There are two challenges with the traditional encoder–decoder framework. First, the encoder needs to compress all the necessary information of a source sentence into a fixed-length vector. Second, it is unable to model the alignment between the source and the target sentences, which is an essential aspect of structured output tasks, such as machine translation. To address these issues, the attention mechanism is introduced to the encoder–decoder model. This mechanism allows the model to align and translate by jointly learning a neural machine translation task. The whose core idea of this mechanism is to induce attention weights over the source sentences to prioritize the set of positions where relevant information is present for generating the next output token. Nowadays, this mechanism has become essential in neural networks, which have been researched for diverse applications. The present survey provides a systematic and comprehensive overview of the developments in attention modeling. The intuition behind attention modeling can be best explained by the simulation mechanism of human visual selectivity, which aims to select more relevant and critical information from tedious information for the current target task while ignoring other irrelevant information in a manner that assists in developing perception. In addition, attention mechanism is an efficient information selection and widely used in deep learning fields in recent years and played a pivotal role in natural language processing, speech recognition, and computer vision. This survey first briefly introduces the origin of the attention mechanism and defines a standard parametric and uniform model for encoder–decoder neural machine translation. Next, various techniques are grouped into coherent categories using types of alignment scores and number of sequences, abstraction levels, positions, and representations. A visual explanation of attention mechanism is then provided to a certain extent, and roles of attention mechanism in multiple application areas is summarized. Finally, this survey identified the future direction and challenges of the attention mechanism.