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: The library design of Torch-Struct follows the distributions API used by both TensorFlow and PyTorch BIBREF29. 
 Question: Does API provide ability to connect to models written in some other deep learning framework?

A: Yes
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Q: Although MCDN doesn't obtain the highest precision, it increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$. 
 Question: What performance did proposed method achieve, how much better is than previous state-of-the-art?

A: increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$
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Q: In this paper, we study verifiable robustness, i.e., providing a certificate that for a given network and test input, no attack or perturbation under the specification can change predictions, using the example of text classification tasks, Stanford Sentiment Treebank (SST) BIBREF15 and AG News BIBREF16.  
 Question: Which dataset do they use?

A:
Stanford Sentiment Treebank (SST) BIBREF15 and AG News BIBREF16
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