TASK DEFINITION: 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.
PROBLEM: 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?

SOLUTION: a vector of frame-level acoustic features

PROBLEM: Table TABREF20 shows that, on WoZ and DSTC2 datasets, SIM model has the same number of parameters, which is only 23% and 19% of that in GLAD model.
Question: How do they measure model size?

SOLUTION: By the number of parameters.

PROBLEM: The primary feed for the analysis collected INLINEFORM0 million tweets containing the keywords 'breast' AND 'cancer'.
Question: How were breast cancer related posts compiled from the Twitter streaming API?

SOLUTION:
By using keywords 'breast' AND 'cancer' in tweet collecting process.