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.

Ex Input:
Given a statement and articles, workers are asked to judge whether the statement can be derived from the articles at three grades: True, Likely (i.e. Answerable), or Unsure (i.e. Unanswerable). If a worker selects Unsure, we ask workers to tell us why they are unsure from two choices (“Not stated in the article” or “Other”). If a worker selects True or Likely in the judgement task, we first ask which sentences in the given articles are justification explanations for a given statement, similarly to HotpotQA BIBREF2. The “summary” text boxes (i.e. NLDs) are then initialized with these selected sentences. 
 Question: How was the dataset annotated?

Ex Output:
True, Likely (i.e. Answerable), or Unsure (i.e. Unanswerable) why they are unsure from two choices (“Not stated in the article” or “Other”) The “summary” text boxes


Ex Input:
Three incremental levels of document preprocessing are experimented with: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document. In doing so, we present the first consistent comparison of different keyphrase extraction models and study their robustness over noisy text. 
 Question: what levels of document preprocessing are looked at?

Ex Output:
raw text text cleaning through document logical structure detection removal of keyphrase sparse sections of the document


Ex Input:
n the OLID dataset, we use a hierarchical annotation model split into three levels to distinguish between whether language is offensive or not (A), and type (B) and target (C) of the offensive language. Each level is described in more detail in the following subsections and examples are shown in Table TABREF10 . Level A: Offensive language Detection
Level A discriminates between offensive (OFF) and non-offensive (NOT) tweets. Level B: Categorization of Offensive Language
Level B categorizes the type of offense and two labels are used: targeted (TIN) and untargeted (INT) insults and threats. Level C: Offensive Language Target Identification
Level C categorizes the targets of insults and threats as individual (IND), group (GRP), and other (OTH). 
 Question: What are the three layers of the annotation scheme?

Ex Output:

Level A: Offensive language Detection
 Level B: Categorization of Offensive Language
 Level C: Offensive Language Target Identification