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.

Input: Consider Input: 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?

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


Input: Consider Input: Macaw also supports Wizard of Oz studies or intermediary-based information seeking studies. The architecture of Macaw for such setup is presented in FIGREF16. As shown in the figure, the seeker interacts with a real conversational interface that supports multi-modal and mixed-initiative interactions in multiple devices. The intermediary (or the wizard) receives the seeker's message and performs different information seeking actions with Macaw. All seeker-intermediary and intermediary-system interactions will be logged for further analysis. 
 Question: What is a wizard of oz setup?

Output: seeker interacts with a real conversational interface intermediary (or the wizard) receives the seeker's message and performs different information seeking actions


Input: Consider Input: We build the iSQuAD and iNewsQA datasets based on SQuAD v1.1 BIBREF0 and NewsQA BIBREF1. iMRC: Making MRC Interactive ::: Evaluation Metric
Since iMRC involves both MRC and RL, we adopt evaluation metrics from both settings. First, as a question answering task, we use $\text{F}_1$ score to compare predicted answers against ground-truth, as in previous works. When there exist multiple ground-truth answers, we report the max $\text{F}_1$ score. Second, mastering multiple games remains quite challenging for RL agents. Therefore, we evaluate an agent's performance during both its training and testing phases. During training, we report training curves averaged over 3 random seeds. During test, we follow common practice in supervised learning tasks where we report the agent's test performance corresponding to its best validation performance . 
 Question: What are the models evaluated on?


Output: They evaluate F1 score and agent's test performance on their own built interactive datasets (iSQuAD and iNewsQA)