Architecture of a BERT-based Encoder-Decoder Model

An NLP team is developing a text summarization system using an encoder-decoder architecture. For the encoder component, they decide to initialize its parameters using a large, pre-trained bidirectional language model that was trained on a massive, general-purpose text corpus. The entire system is then fine-tuned on their specific summarization dataset. What is the primary advantage of this strategy compared to training the encoder from scratch?

Training Strategy for a BERT-based Encoder

When adapting a pre-trained bidirectional language model to serve as the encoder in a sequence-to-sequence architecture for a task like machine translation, it is standard practice to freeze the encoder's parameters and only train the randomly initialized decoder.