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.

Example Input:  We defined a sequence labeling task to extract custom entities from user input. We assumed seven (7) possible entities (see Table TABREF43) to be recognized by the model: topic, subtopic, examination mode and level, question number, intent, as well as the entity other for remaining words in the utterance.   We defined a classification problem to predict the system's next action according to the given user input. We assumed 13 custom actions (see Table TABREF42) that we considered being our labels. In the conversational dataset, each input was automatically labeled by the rule-based system with the corresponding next action and the dialogue-id. Thus, no additional post-labeling was required.  
 Question: How does the IPA label data after interacting with users?
Example Output: It defined a sequence labeling task to extract custom entities from user input and label the next action (out of 13  custom actions defined).

Example Input: It is interesting to observe that the baseline model amplifies the bias in the training data set as measured by INLINEFORM0 and INLINEFORM1 . From measurements using the described bias metrics, our method effectively mitigates bias in language modelling without a significant increase in perplexity. 
 Question: how is mitigation of gender bias evaluated?
Example Output: Using INLINEFORM0 and INLINEFORM1

Example Input: The multi-task model outperforms the single-task model at all data sizes, and the relative performance increases as the size of the training data decreases. When only 200 sentences of training data are used, the performance of the multi-task model is about 60% better than the single-task model for both the Airbnb and Greyhound apps. The relative gain for the OpenTable app is 26%. The results differ between the tasks, but none have an overall benefit from the open vocabulary system. 
 Question: Does the performance increase using their method?
Example Output:
The multi-task model outperforms the single-task model at all data sizes but none have an overall benefit from the open vocabulary system