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: While the dataset was collected with simulated environments, no structure was imposed on the navigation instructions while crowd-sourcing data. Thus, many instructions in our dataset are ambiguous. Moreover, the order of the behaviors in the instructions is not always the same. For instance, a person said “turn right and advance” to describe part of a route, while another person said “go straight after turning right” in a similar situation. The high variability present in the natural language descriptions of our dataset makes the problem of decoding instructions into behaviors not trivial. See Appendix A of the supplementary material for additional details on our data collection effort. 
 Question: What language is the experiment done in?

Output: english language


Input: Consider Input:  For a fair comparison with previous work, we train the baseline MT system on the data released by BIBREF11.  
 Question: what was the baseline?

Output:  MT system on the data released by BIBREF11


Input: Consider Input:  We apply an off-the-shelf tool for emotion recognition (the manufacturer cannot be disclosed due to licensing restrictions). It delivers frame-by-frame scores ($\in [0;100]$) for discrete emotional states of joy, anger and fear. We extract the audio signal for the same sequence as described for facial expressions and apply an off-the-shelf tool for emotion recognition. The software delivers single classification scores for a set of 24 discrete emotions for the entire utterance. 
 Question: What are the emotion detection tools used for audio and face input?


Output: We apply an off-the-shelf tool for emotion recognition (the manufacturer cannot be disclosed due to licensing restrictions)