Detailed Instructions: In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
See one example below:
Problem: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.
Question: Are results reported only for English data?
Solution: No
Explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

Problem: 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?
Solution:
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.