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BERTS2S
BERTS2S, short for BERT sequence-to-sequence, is a neural network architecture designed for conditional text generation tasks such as abstractive document summarization and machine translation. Built on the standard Transformer encoder-decoder framework, the architecture leverages transfer learning by initializing both the encoder and decoder with pretrained Bidirectional Encoder Representations from Transformers checkpoints rather than initializing them randomly. In this setup, the encoder and autoregressive decoder typically share pretrained parameter weights, while the intervening encoder-decoder cross-attention layers are initialized randomly and learned during fine-tuning on the target generation task. By adapting rich bidirectional language representations to a sequence-to-sequence pipeline, BERTS2S substantially improves output fluency, semantic coverage, and factual consistency compared to standard sequence models trained from scratch.
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