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 Q]: We use the VisDial v1.0 BIBREF0 dataset to train our models, where one example has an image with its caption, 9 question-answer pairs, and follow-up questions and candidate answers for each round. At round $r$, the caption and the previous question-answer pairs become conversational context. The whole dataset is split into 123,287/2,000/8,000 images for train/validation/test, respectively. Unlike the images in the train and validation sets, the images in the test set have only one follow-up question and candidate answers and their corresponding conversational context. 
 Question: How big is dataset for this challenge?
[EX A]: 133,287 images

[EX Q]: In a nutshell, B4MSA starts by applying text-transformations to the messages, then transformed text is represented in a vector space model (see Subsection SECREF13 ), and finally, a Support Vector Machine (with linear kernel) is used as the classifier. 
 Question: What are the components of the multilingual framework?
[EX A]: text-transformations to the messages vector space model Support Vector Machine

[EX Q]: However, we also showed difference in produced errors for each method and different impact at word-level depending of the approach or units. Thus, future work will focus on analysing the orthographic output of these systems in two ways: 1) investigate errors produced by the end-to-end methods and explore several approaches to correct common errors done in French and 2) compare the end-to-end methods in a SLU context and evaluate the semantic value of the partially correct produced words. 
 Question: What will be in focus for future work?
[EX A]:
1) investigate errors produced by the end-to-end methods and explore several approaches to correct common errors done in French 2) compare the end-to-end methods in a SLU context and evaluate the semantic value of the partially correct produced words