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: 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?

Answer: German-English, French-English, and Japanese-English


Question:  Impact votes are provided by the users of the platform to evaluate how impactful a particular claim is. Users can pick one of 5 possible impact labels for a particular claim: no impact, low impact, medium impact, high impact and very high impact. While evaluating the impact of a claim, users have access to the full argument context and therefore, they can assess how impactful a claim is in the given context of an argument. 
 Question: What annotations are available in the dataset?

Answer: 5 possible impact labels for a particular claim: no impact, low impact, medium impact, high impact and very high impact


Question: Text Representation.
The model, implemented as a deep neural network, learns to respond by training on hundreds of millions context-reply $(c,r)$ pairs. First, similar to Henderson:2017arxiv, raw text from both $c$ and $r$ is converted to unigrams and bigrams. Photo Representation.
Photos are represented using convolutional neural net (CNN) models pretrained on ImageNet BIBREF17. We use a MobileNet model with a depth multiplier of 1.4, and an input dimension of $224 \times 224$ pixels as in BIBREF18. 
 Question: How does PolyResponse architecture look like?

Answer:
Henderson:2017 MobileNet model