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: 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?

Output: a vector of frame-level acoustic features


Input: Consider Input: We evaluated our models on 3 different datasets:

CSAT dataset for CSAT prediction, consisting of spoken transcripts (automatic via ASR).

20 newsgroups for topic identification task, consisting of written text;

Fisher Phase 1 corpus for topic identification task, consisting of spoken transcripts (manual); 
 Question: What datasets did they use for evaluation?

Output: CSAT dataset 20 newsgroups Fisher Phase 1 corpus


Input: Consider Input: We now seek to know if a pre-trained multi-BERT has ability to solve RC tasks in the zero-shot setting. 
 Question: What model is used as a baseline?  


Output: pre-trained multi-BERT