TASK DEFINITION: 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.
PROBLEM: The dataset consists of more than $0.3$ million records and has been made available for future research. 
 Question: What is the size of the dataset?

SOLUTION: $0.3$ million records

PROBLEM: 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?

SOLUTION: no evidence of depression depressed mood disturbed sleep fatigue or loss of energy

PROBLEM: In this section, we consider two kinds of relational classification tasks: (1) relation prediction and (2) relation extraction.  We hope to design a simple and clear experiment setup to conduct error analysis for relational prediction. Therefore, we consider a typical method TransE BIBREF3 as the subject as well as FB15K BIBREF3 as the dataset. For relation extraction, we consider the supervised relation extraction setting and TACRED dataset BIBREF10 . As for the subject model, we use the best model on TACRED dataset — position-aware neural sequence model.  
 Question: Which competitive relational classification models do they test?

SOLUTION:
For relation prediction they test TransE and for relation extraction they test position aware neural sequence model