instruction:
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.
question:
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?
answer:
No

question:
A few sample advice rules in English (these are converted to first-order logic format and given as input to our algorithm) are presented in Table TABREF11 . We modified the work of Odom et al. odomAIME15,odomAAAI15 to learn RDNs in the presence of advice. The key idea is to explicitly represent advice in calculating gradients.
Question: How do they incorporate human advice?
answer:
by converting human advice to first-order logic format and use as an input to calculate gradient

question:
We collect HLA data from TV Tropes BIBREF3, a knowledge-based website dedicated to pop culture, containing information on a plethora of characters from a variety of sources. Similar to Wikipedia, its content is provided and edited collaboratively by a massive user-base. These attributes are determined by human viewers and their impressions of the characters, and are correlated with human-like characteristics. We believe that TV Tropes is better for our purpose of fictional character modeling than data sources used in works such as BIBREF25 shuster2019engaging because TV Tropes' content providers are rewarded for correctly providing content through community acknowledgement.
Question: How does dataset model character's profiles?
answer:
attributes are determined by human viewers and their impressions of the characters, and are correlated with human-like characteristics