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: Experimental Setup
Question: what english datasets were used?
Example Output: Answer with content missing: (Data section) Penn Treebank (PTB)

Example Input: 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
brace \subseteq \lbrace 1,..,M-1
brace $. 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?
Example Output: No

Example Input: To implement the first tier of our framework and detect moral relevance, we complement our morally relevant seed words with a corresponding set of seed words approximating moral irrelevance based on the notion of valence, i.e., the degree of pleasantness or unpleasantness of a stimulus. We refer to the emotional valence ratings collected by BIBREF28 for approximately 14,000 English words, and choose the words with most neutral valence rating that do not occur in the MFD as our set of morally irrelevant seed words, for an equal total number of morally relevant and morally irrelevant words.
Question: How do they quantify moral relevance?
Example Output:
By complementing morally relevant seed words with a set of morally irrelevant seed words based on the notion of valence