Autoencoding Models - BERT

Previous Methods

Methodology

Masked Language Model

Next Sentence Prediction

Embedding Combination

Fine-tuning

\[\begin{gathered} \begin{aligned} P(\cdot|x;\theta_{\text{PLM}},W)&=\text{softmax}(h\cdot{W}) \\ &=\text{softmax}\Big(\text{PLM}(x;\theta_\text{PLM})\cdot{W}\Big), \end{aligned} \\ \text{where }h\in\mathbb{R}^{\text{hidden}\_\text{size}}\text{ and }W\in\mathbb{R}^{\text{hidden}\_\text{size}\times\text{\#classes}}. \end{gathered}\]

Evaluations

\[\begin{gathered} \mathcal{D}=\{(x_i,s_i,e_i)\}_{i=1}^N, \\ \text{where }x_i\text{ is input and }s_i\text{ is start index of span with end index of span }e_i. \\ \mathcal{L}_\text{span}(\theta_\text{PLM},S,E)=-\sum_{i=1}^N{ \log{P(s_i|x_i;\theta_\text{PLM},S)} }-\sum_{i=1}^N{ \log{P(e_i|x_i;\theta_\text{PLM},E)} } \\ \\ P(s_i|x_i;\theta_\text{PLM},S)=\frac{\exp(S^\intercal\cdot{h_i})}{\sum_{j=1}^\ell{ \exp(S^\intercal\cdot{h_j}) }} \\ P(e_i|x_i;\theta_\text{PLM},E)=\frac{\exp(E^\intercal\cdot{h_i})}{\sum_{j=1}^\ell{ \exp(E^\intercal\cdot{h_j}) }} \end{gathered}\]

Wrap-up

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