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Evaluating Attention Mechanisms in Machine Translation
Based on the mathematical form of Function A, explain the most likely reason for its lower performance on complex tasks compared to Function B.
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In a sequence-to-sequence model, an attention mechanism calculates a score for three input vectors (A, B, and C) relative to a single output vector (D). The scoring function is the simple dot product between the output vector and each input vector. You are given the following geometric relationships:
- Vector A points in a very similar direction to Vector D.
- Vector B is orthogonal (at a 90-degree angle) to Vector D.
- Vector C points in the opposite direction of Vector D.
Which input vector
Evaluating Attention Mechanisms in Machine Translation
Calculating a Dot Attention Score