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.

Input: Consider Input: Suppose there are INLINEFORM0 mentions in documents on average, among these global models, NCEL not surprisingly has the lowest time complexity INLINEFORM1 since it only considers adjacent mentions, where INLINEFORM2 is the number of sub-GCN layers indicating the iterations until convergence. 
 Question: Do they only use adjacent entity mentions or use more than that in some cases (next to adjacent)?

Output: NCEL considers only adjacent mentions.


Input: Consider Input: Our attempt at language pre-training fell short of our expectations in all but one tested dataset. Our pre-training was unsuccessful in improving accuracy, even when applied to networks larger than those reported. 
 Question: Does pre-training on general text corpus improve performance?

Output: No


Input: Consider Input: These features are typically obtained by training a deep neural network jointly on several languages for which labelled data is available. The bottom layers of the network are normally shared across all training languages. The network then splits into separate parts for each of the languages, or has a single shared output. The final output layer has phone labels or HMM states as targets. The final shared layer often has a lower dimensionality than the input layer, and is therefore referred to as a `bottleneck'. 
 Question: What are bottleneck features?


Output: Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese South African English These features are typically obtained by training a deep neural network jointly on several languages for which labelled data is available. The final shared layer often has a lower dimensionality than the input layer, and is therefore referred to as a `bottleneck'.