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.

Q: We also run a manual evaluation which shows for the En-It task a slight quality degradation in exchange of a statistically significant reduction in the average length ratio, from 1.05 to 1.01.
Question: Do they conduct any human evaluation?

A: Yes
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Q: Maximum Entropy theory is applied to solve Vietnamese word segmentation BIBREF15 , BIBREF22 , BIBREF23 . There are several studies about the Vietnamese word segmentation task over the last decade. Dinh et al. started this task with Weighted Finite State Transducer (WFST) approach and Neural Network approach BIBREF9 . In addition, machine learning approaches are studied and widely applied to natural language processing and word segmentation as well. In fact, several studies used support vector machines (SVM) and conditional random fields (CRF) for the word segmentation task BIBREF7 , BIBREF8 .
Question: Which approaches have been applied to solve word segmentation in Vietnamese?

A: Maximum Entropy Weighted Finite State Transducer (WFST) support vector machines (SVM) conditional random fields (CRF)
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Q: 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?

A:
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.
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