Transformers in Natural Language Processing
Résumé
This chapter presents an overview of the state of the art in natural language processing, exploring one specific computational architecture, the Transformer model, which plays a central role in a wide range of applications. This architecture condenses many advances in neural learning methods and can be exploited in many ways : to learn representations for linguistic entities ; to generate coherent utterances and answer questions ; to perform utterance transformations, an illustration being their automatic translation. These different facets of the architecture will be successively presented, also allowing us to discuss its limitations.
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