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.

Experiments ::: Baselines
For comparison, we select several public models as baselines including semantic parsing models: BiDAF BIBREF3, an MRC model which utilizes a bi-directional attention flow network to encode the question and passage;

QANet BIBREF12, which utilizes convolutions and self-attentions as the building blocks of encoders to represent the question and passage;

BERT BIBREF23, a pre-trained bidirectional Transformer-based language model which achieves state-of-the-art performance on lots of public MRC datasets recently;

and numerical MRC models: NAQANet BIBREF6, a numerical version of QANet model.

NAQANet+, an enhanced version of NAQANet implemented by ourselves, which further considers real number (e.g. “2.5”), richer arithmetic expression, data augmentation, etc. Syn Dep BIBREF6, the neural semantic parsing model (KDG) BIBREF22 with Stanford dependencies based sentence representations;

OpenIE BIBREF6, KDG with open information extraction based sentence representations;

SRL BIBREF6, KDG with semantic role labeling based sentence representations;

and traditional MRC models: 
 Question: what are the existing models they compared with?
Syn Dep OpenIE SRL BiDAF QANet BERT NAQANet NAQANet+

The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. Table presents the results for bilingual UWS and multilingual leveraging.  
 Question: Does the paper report the accuracy of the model?
No

MTMSN BIBREF4 is the first, and only model so far, that specifically tried to tackle the multi-span questions of DROP.  
 Question: What is the previous model that attempted to tackle multi-span questions as a part of its design?

MTMSN BIBREF4