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: 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)?
Example Output: NCEL considers only adjacent mentions.

Example Input: Last, we evaluate our approaches in 9 commonly used text classification datasets. We evaluate our methods on several commonly used datasets whose themes range from sentiment, web-page, science to medical and healthcare.
Question: What NLP tasks do they consider?
Example Output: text classification for themes including sentiment, web-page, science, medical and healthcare

Example Input: We use three encoder-decoder neural architectures as baselines: (1) LSTM+attention as an LSTM BIBREF19 with attention mechanism BIBREF20; (2) Transformer BIBREF21 and (3) Universal Transformer BIBREF22.
Question: What three machine architectures are analyzed?
Example Output:
LSTM+attention Transformer Universal Transformer