Encoder-Decoder Models
Class of deep learning architectures that process an input to generate a corresponding output.
Encoder-decoder models, often integral to natural language processing (NLP), consist of two main components: an encoder that processes the input data and converts it into a fixed-dimensional context vector, and a decoder that takes this vector to generate the output data. This architecture is especially effective in managing the sequence-to-sequence tasks where the input and output lengths can vary. For example, in machine translation, the encoder processes a sentence in the source language and the decoder generates a translation in the target language. The success of these models largely hinges on their ability to capture contextual information and dependencies within the data, which is critical for generating coherent and contextually appropriate outputs.
The concept of encoder-decoder models became prominent around 2014, particularly with the advent of the sequence-to-sequence learning framework that was pivotal in improving machine translation systems.
Notable advancements in encoder-decoder models were made by researchers like Ilya Sutskever, Oriol Vinyals, and Quoc Le, among others, who contributed to foundational papers and development of early models that showcased the effectiveness of this architecture in complex language tasks. Their work laid the groundwork for further research and application across different domains of AI.