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.
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Question:  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?

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


Question: We also applied our model to MCTest dataset which requires machines to answer multiple-choice reading comprehension questions about fictional stories.  
 Question: Do they experiment with their proposed model on any other dataset other than MovieQA?

Answer: Yes


Question: We employ the standard ROUGE-1, ROUGE-2 and ROUGE-L metrics BIBREF29 to evaluate all summarization models. Following BIBREF22, BIBREF23, we use F-measure ROUGE on XSUM and CNN/DailyMail, and use limited-length recall-measure ROUGE on NYT and DUC. 
 Question: What metric was used in the evaluation step?

Answer:
ROUGE-1, ROUGE-2 and ROUGE-L F-measure ROUGE on XSUM and CNN/DailyMail, and use limited-length recall-measure ROUGE on NYT and DUC