Sequence masking is crucial in training models on variable-length sequences, such as sentences or time series data, where padding is added to standardize input lengths. In natural language processing (NLP), for instance, sequence masking ensures that the padding tokens do not affect the model’s learning by creating a mask that identifies these tokens and instructs the model to ignore them during training and inference. This is vital for attention mechanisms in transformers, where the mask can prevent the model from attending to padded positions, thereby focusing only on meaningful parts of the sequence. Additionally, masking can be used to hide future tokens in sequence-to-sequence models during training to ensure proper autoregressive prediction.
The concept of sequence masking has been present since the early 2000s with the advent of sequence-based models like Recurrent Neural Networks (RNNs). It gained significant attention and importance with the introduction of transformer models in 2017, particularly with the "Attention is All You Need" paper by Vaswani et al., which emphasized the need for effective masking strategies to handle variable-length sequences and facilitate efficient attention mechanisms.
The key contributors to the development and popularization of sequence masking include the authors of the seminal paper "Attention is All You Need"—Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. Their work on transformers and the attention mechanism significantly highlighted and advanced the use of sequence masking in modern NLP models.