TASK DEFINITION: In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
PROBLEM: In phase 1 (also referred to as the “exploration” phase) the algorithm explores the state space through keeping track of previously visited states by maintaining an archive. During this phase, instead of resuming the exploration from scratch, the algorithm starts exploring from promising states in the archive to find high performing trajectories. 
 Question: How is trajectory with how rewards extracted?

SOLUTION: Extractive

PROBLEM: We employ a huge dataset of 45,000 self-reported depressed users introduced in BIBREF46 where a lexicon of depression symptoms consisting of 1500 depression-indicative terms was created with the help of psychologist clinician and employed for collecting self-declared depressed individual's profiles. A subset of 8,770 users (24 million time-stamped tweets) containing 3981 depressed and 4789 control users (that do not show any depressive behavior) were verified by two human judges BIBREF46 . 
 Question: How is the data annotated?

SOLUTION: Abstractive

PROBLEM: Compared to using external models for confidence modeling, an advantage of the proposed method is that the base model does not change: the binary classification loss just provides additional supervision. For supervised open IE systems, the confidence score of an assertion is typically computed based on its extraction likelihood given by the model BIBREF3 , BIBREF5  We follow the evaluation metrics described by Stanovsky:2016:OIE2016: area under the precision-recall curve (AUC) and F1 score. 
 Question: How does this compare to traditional calibration methods like Platt Scaling?

SOLUTION:
Abstractive