Transformers: Deep Neural Network Architectures for Natural Language Processing

Language technologies feature prominently among the applications of artificial intelligence (AI) and are now reaching the general public. They are essential for effectively accessing textual information available on the web or in large document databases. They enable new forms of interaction with machines, whether through voice commands or via tools that assist with typing or writing. They also facilitate communication with other humans, for example through machine translation systems. Behind the scenes, these algorithms organize and filter the vast amount of text and audio recordings circulating on the web and social media. In this way, they are transforming how this data is managed.

This transition has accelerated as these technologies have become more powerful, enabling an ever-wider and more diverse range of applications. These advances stem from a combination of several factors: on the one hand, the development of increasingly sophisticated machine learning algorithms capable of leveraging improvements in computing hardware; on the other hand, the ability to access vast amounts of textual data—whether annotated or unannotated—to enable this learning.

Among these algorithms, neural networks—particularly the Transformer architecture—take center stage. This architecture has become essential for performing three types of processing that previously required dedicated components. First, text mining and information retrieval algorithms benefit from the rich internal representations computed by this model. Second, linguistic analysis algorithms leverage the Transformers’ ability to account for very long-range dependencies. Finally, text generation algorithms use these models, primarily for their predictive capabilities.

Furthermore, this architecture is well-suited for processing spoken and even multimodal data, and enables efficient large-scale computations. It’s easy to see why this model has become the language engineer’s go-to tool.