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: 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?

Output: Using INLINEFORM0 and INLINEFORM1

Input: Consider Input: While the performances of a purely content-based model naturally stays stable, the performance of the other systems decrease notably they perform worse than the content-based model.
Question: How do they demonstrate the robustness of their results?

Output: performances of a purely content-based model naturally stays stable

Input: Consider Input: To exemplify the INLINEFORM0 score we evaluated and compared the performance of two different SBD systems over a set of YouTube videos in a multi-reference enviroment. The first system (S1) employs a Convolutional Neural Network to determine if the middle word of a sliding window corresponds to a SU boundary or not BIBREF30 . The second approach (S2) by contrast, introduces a bidirectional Recurrent Neural Network model with attention mechanism for boundary detection BIBREF31 .
Question: Which SBD systems did they compare?
Output: Convolutional Neural Network bidirectional Recurrent Neural Network model with attention mechanism