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:
Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De). 
 Question: What dataset do they use?

Ex Output:
English$\rightarrow $Italian/German portions of the MuST-C corpus As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De)


Ex Input:
We apply the Generative Pre-trained Transformer (GPT) BIBREF2 as our pre-trained language model.  
 Question: What pretrained LM is used?

Ex Output:
Generative Pre-trained Transformer (GPT)


Ex Input:
In this work, we propose to augment the integer linear programming (ILP)-based summarization framework with a low-rank approximation of the co-occurrence matrix, and further evaluate the approach on a broad range of datasets exhibiting high lexical diversity. 
 Question: What do they constrain using integer linear programming?

Ex Output:

low-rank approximation of the co-occurrence matrix