In a neural text-classification pipeline, tokens are first converted to integer IDs with a string-indexing layer. The engineer reserves index 0 for a padding token, index 1 for an out-of-vocabulary (OOV) token, and assigns the remaining vocabulary sequentially from index 2 upward. What is the main advantage of reserving these low consecutive indices for the two special tokens when the data later flows into an Embedding layer?
It lets the text-vectorization step compute TF-IDF weights for the special tokens automatically, eliminating manual weighting.
It enables the framework to mask or zero-out the padding and OOV rows efficiently, preventing those tokens from affecting gradients during training or predictions.
It reduces the dimensionality of the embedding space by two, which significantly lowers memory consumption.
It forces the model to treat padding and OOV tokens as high-frequency words, accelerating convergence on small datasets.
Deep-learning frameworks treat the embedding row at the padding index as a masked or zero vector and can skip gradient updates for it; the same table position can be excluded for the OOV token. By fixing these sentinel IDs at known, low positions, the runtime can efficiently mask them during forward and backward passes, so neither padding nor unseen words influence the model's parameters. Simply shrinking the embedding size, re-weighting, or computing TF-IDF does not provide this masking behavior.
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Why is the padding token assigned to index 0 in a neural network pipeline?
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What is the purpose of the out-of-vocabulary (OOV) token in text classification?
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How does masking influence gradient calculation during training?