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:
We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English.  
 Question: Which languages are used in the multi-lingual caption model?

Ex Output:
German-English, French-English, and Japanese-English


Ex Input:
 As BIBREF0 discussed, the text-only UMT is fundamentally an ill-posed problem, since there are potentially many ways to associate target with source sentences. Intuitively, since the visual content and language are closely related, the image can play the role of a pivot “language" to bridge the two languages without paralleled corpus, making the problem “more well-defined" by reducing the problem to supervised learning. 
 Question: Why is this work different from text-only UNMT?

Ex Output:
the image can play the role of a pivot “language" to bridge the two languages without paralleled corpus


Ex Input:
The regressor and classifier used Support Vector Regression (SVR) and Support Vector Classification (SVC) respectively. The table includes a neural regressor (NNR) and a neural classifier (NNC). The neural classifier is trained in two set ups: “NNC top 5” uses classification labels as described in Section SECREF3, and “NNC SU4 F1” uses the regression labels, that is, the ROUGE-SU4 F1 scores of each sentence. 
 Question: What classification approaches were experimented for this task?

Ex Output:

NNC SU4 F1 NNC top 5 Support Vector Classification (SVC)