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.

Ex Input:
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?

Ex Output:
increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$

Ex Input:
To be able to employ the trained model, test sets are first translated to English via machine translation and then inference takes place. For machine translation, Google translation API is used.
Question: how did the authors translate the reviews to other languages?

Ex Output:
Using Google translation API.

Ex Input:
For testing, we also applied the TongueNet on the UBC database BIBREF14 without any training to see the generalization ability of the model.
Question: What previously annotated databases are available?

Ex Output:
the UBC database BIBREF14