instruction:
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.
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:
The observed performance of both the BasicDCGAN and MultitaskDCGAN using 3-classes is comparable to the state-of-the-art, with 49.80% compared to 49.99% reported in BIBREF16  
 Question: What model achieves state of the art performance on this task?
answer:
BIBREF16


question:
Data-driven models for morphological analysis are constructed using training data INLINEFORM0 consisting of INLINEFORM1 training examples. The baseline model BIBREF5 we compare with regards the output space of the model as a subset INLINEFORM2 where INLINEFORM3 is the set of all tag sets seen in this training data. Specifically, they solve the task as a multi-class classification problem where the classes are individual tag sets. In low-resource scenarios, this indicates that INLINEFORM4 and even for those tag sets existing in INLINEFORM5 we may have seen very few training examples. The conditional probability of a sequence of tag sets given the sentence is formulated as a 0th order CRF. DISPLAYFORM0 
 Question: What other cross-lingual approaches is the model compared to?
answer:

The baseline model BIBREF5 we compare with regards the output space of the model as a subset INLINEFORM2 where INLINEFORM3 is the set of all tag sets seen in this training data.