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]: Suppose there are $M$ total layers, and define a subset of these layers at which to apply the loss function: $K = \lbrace k_1, k_2, ..., k_L\rbrace \subseteq \lbrace 1,..,M-1\rbrace $. The total objective function is then defined as

where $Z_{k_l}$ is the $k_l$-th Transformer layer activations, $Y$ is the ground-truth transcription for CTC and context dependent states for hybrid ASR, and $Loss(P, Y)$ can be defined as CTC objective BIBREF11 or cross entropy for hybrid ASR. T 
 Question: Do they just sum up all the loses the calculate to end up with one single loss?
[EX A]: No

[EX Q]: The second data batch consists of event-related tweets for five natural disasters occurring in the U.S. in 2018. These are: the East Coast Bomb Cyclone (Jan. 2 - 6); the Mendocino, California wildfires (Jul. 27 - Sept. 18); Hurricane Florence (Aug. 31 - Sept. 19); Hurricane Michael (Oct. 7 - 16); and the California Camp Fires (Nov. 8 - 25).  
 Question: Which five natural disasters were examined?
[EX A]: the East Coast Bomb Cyclone  the Mendocino, California wildfires Hurricane Florence Hurricane Michael the California Camp Fires

[EX Q]: These actions consist of the following components:

[leftmargin=*]

Co-Reference Resolution: To support multi-turn interactions, it is sometimes necessary to use co-reference resolution techniques for effective retrieval. Query Generation: This component generates a query based on the past user-system interactions. Retrieval Model: This is the core ranking component that retrieves documents or passages from a large collection. Result Generation: The retrieved documents can be too long to be presented using some interfaces. 
 Question: What are the different modules in Macaw?
[EX A]:
Co-Reference Resolution Query Generation Retrieval Model Result Generation