Task: Question Answering
Input (English): Our encoder-decoder framework employs separate encoding for different speakers in the dialog. User utterances $x_t^{usr}$ and system utterances $x_t^{sys}$ are fed into a user encoder and a system encoder separately to obtain encoder hidden states $h_{i}^{usr}$ and $h_{i}^{sys}$ . We integrate semantic slot scaffold by performing delexicalization on original dialogs. Delexicalization is a common pre-processing step in dialog modeling. We integrate dialog domain scaffold through a multi-task framework. Dialog domain indicates different conversation task content, for example, booking hotel, restaurant and taxi in MultiWOZ dataset. 
 Question: How does SPNet utilize additional speaker role, semantic slot and dialog domain annotations?
Output (English): Our encoder-decoder framework employs separate encoding for different speakers in the dialog. We integrate semantic slot scaffold by performing delexicalization on original dialogs. We integrate dialog domain scaffold through a multi-task framework.