TASK DEFINITION: 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.
PROBLEM:  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?

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

PROBLEM: For DQA four participants answered each question, therefore we took the average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values over the four evaluators as the result per question. 
 Question: How do they measure performance?

SOLUTION: average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values

PROBLEM: In our experiments, we used TF.IDF-based features over the title and over the content of the article we wanted to classify. We had these features twice – once for the title and once for the the content of the article, as we wanted to have two different representations of the same article. Thus, we used a total of 1,100 TF.IDF-weighted features (800 content + 300 title), limiting the vocabulary to the top 800 and 300 words, respectively (which occurred in more than five articles). 
 Question: what lexical features did they experiment with?

SOLUTION:
TF.IDF-based features