An attention mechanism acts as a learnable pointer structure that allows a model to dynamically focus on specific parts of a sequence. This solves the challenge of increasing a system's memory and complexity without adding learnable parameters, and bypasses the bottleneck of compressing an entire sequence into a fixed-dimensional representation.

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Sequence learning encompasses machine learning problems that require a model to ingest sequences of inputs, emit sequences of outputs, or both. Unlike standard tasks that process a fixed number of inputs independently, sequence learning models maintain context across successive observations, making them essential for handling varying-length data such as video snippets, text, or patient histories over time.

Sequence Learning

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Sequence-to-sequence learning is a specific subset of sequence learning problems where both the inputs and the outputs consist of variable-length sequences. In these tasks, the length of the input sequence does not necessarily match the length of the output sequence. Prominent applications include machine translation and speech-to-text transcription.

Sequence-to-Sequence Learning

Sequence tagging and parsing are sequence learning tasks that involve annotating a text sequence with specific attributes. In these problems, the inputs and outputs are strictly aligned, meaning they share the exact same number of elements and occur in a corresponding order. An example is part-of-speech tagging, where every word in a sentence is annotated with its corresponding grammatical role.

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