Seq2Seq Model

缺陷:

​ 由于Decoder的输入是且仅是Encoder输出在最后的\(H_m\), 因此可能会丢失部分前面的信息, 并在序列越长此问题越严重.

Attention 如何改进Seq2Seq model的遗忘问题

SimpleRNN + Attention:

• \(S_0\)现在不仅是最后一个\(h_m\), 而是对于每一个\(h_i\)都给予一个\(weight: \alpha_i = align(h_i, s_0)\).
• \(Context \space vector: c_0 = \sum\limits_{i=1}^{m}\alpha_i h_m\).

如何计算该weight:

1. Used in original paper:

   \(\tilde{\alpha_i} = v^T \cdot tanh[w \cdot concat(h_i, s_0)]\), then normalize the weights with softmax.\(v^T, w\)为trainable parameters.

2. A more popular one, used in Transformer:

   1. Linear maps:

      • \(k_i = W_k \cdot h_i\), for i = 1 to m.
      • \(q_0 = W_q \cdot s_0\).

   2. Inner Product:

      \(\tilde{\alpha_i} = k_i^T q_0\), for i = 1 to m.

   3. Normalization with softmax.

时间复杂度: Encoder状态数 x Decoder状态数

Reference: shusen wang 老师讲解(强力推荐) https://www.youtube.com/watch?v=aButdUV0dxI&list=PLvOO0btloRntpSWSxFbwPIjIum3Ub4GSC