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 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\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 Output:
No


Ex Input:
To further improve the performance, we adopt system combination on the decoding lattice level. By combining systems, we can take advantage of the strength of each model that is optimized for different domains.  The best result for vlsp2018 of 4.85% WER is obtained by the combination weights 0.6:0.4 where 0.6 is given to the general language model and 0.4 is given to the conversation one. On the vlsp2019 set, the ratio is change slightly by 0.7:0.3 to deliver the best result of 15.09%. 
 Question: What is the language model combination technique used in the paper?

Ex Output:
system combination on the decoding lattice level combination weights


Ex Input:
In this paper we focus on the skipgram approach with random negative examples proposed in BIBREF0 .  
 Question: Do they use skipgram version of word2vec?

Ex Output:

Yes