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.

[EX Q]: 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?
[EX A]: a vector of frame-level acoustic features

[EX Q]: To address this drawback in ROUGE, we propose a new evaluation metric: Critical Information Completeness (CIC). Formally, CIC is a recall of semantic slot information between a candidate summary and a reference summary. CIC is defined as follows:

where $V$ stands for a set of delexicalized values in the reference summary, $Count_{match}(v)$ is the number of values co-occurring in the candidate summary and reference summary, and $m$ is the number of values in set $V$. In our experiments, CIC is computed as the arithmetic mean over all the dialog domains to retain the overall performance.

CIC is a suitable complementary metric to ROUGE because it accounts for the most important information within each dialog domain. CIC can be applied to any summarization task with predefined essential entities. 
 Question: How does new evaluation metric considers critical informative entities?
[EX A]: Answer with content missing: (formula for CIC) it accounts for the most important information within each dialog domain. CIC can be applied to any summarization task with predefined essential entities

[EX Q]: We first run our experiment on BiLSTM, BiLSTM-CNN, BiLSTM-CRF BiLSTM-CNN-CRF using the hyper-parameters mentioned in Table TABREF30.  
 Question: Which machine learning models do they explore?
[EX A]:
BiLSTM BiLSTM-CNN BiLSTM-CRF BiLSTM-CNN-CRF