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: Table TABREF14 show the results obtained for proposed s2sL approach in comparison to that of MLP for the tasks of Speech/Music and Neutral/Sad classification, by considering different proportions of training data. 
 Question: Which models/frameworks do they compare to?
Example Output: MLP

Example Input: We apply the Generative Pre-trained Transformer (GPT) BIBREF2 as our pre-trained language model.  
 Question: What pretrained LM is used?
Example Output: Generative Pre-trained Transformer (GPT)

Example Input: An acoustic word embedding is a function that takes as input a speech segment corresponding to a word, INLINEFORM0 , where each INLINEFORM1 is a vector of frame-level acoustic features, and outputs a fixed-dimensional vector representing the segment, INLINEFORM2 . 
 Question: How do they represent input features of their model to train embeddings?
Example Output:
a vector of frame-level acoustic features