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.

Q: Each tweet is annotated as no evidence of depression (e.g., “Citizens fear an economic depression") or evidence of depression (e.g., “depressed over disappointment"). If a tweet is annotated evidence of depression, then it is further annotated with one or more depressive symptoms, for example, depressed mood (e.g., “feeling down in the dumps"), disturbed sleep (e.g., “another restless night"), or fatigue or loss of energy (e.g., “the fatigue is unbearable") BIBREF10 . 
 Question: How is the dataset annotated?

A: no evidence of depression depressed mood disturbed sleep fatigue or loss of energy
****
Q: Our data set contains $3,209$ reviews about 553 different cars from 49 different car manufacturers. 
 Question: How big is dataset of car-speak language?

A: $3,209$ reviews 
****
Q: Architecture-based methods either try to induce task-specific architecture during pre-training (task-specific methods), or aim at building a general pre-training architecture to fit all downstream tasks (task-agnostic methods) 
 Question: How architecture-based method handle obstacles in NLG?

A:
task-specific architecture during pre-training (task-specific methods) aim at building a general pre-training architecture to fit all downstream tasks (task-agnostic methods)
****