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.

Input: Consider Input: jiant System Overview ::: jiant Components
Tasks: Tasks have references to task data, methods for processing data, references to classifier heads, and methods for calculating performance metrics, and making predictions. 
 Question: Does jiant involve datasets for the 50 NLU tasks?

Output: Yes


Input: Consider 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?

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


Input: Consider Input: We explored different normalization techniques. FBanks with cepstral mean normalization (CMN) perform better than raw FBanks. We found using variance with mean normalization (CMVN) unnecessary for the task. Using deltas and delta-deltas improves model, so we used them in other experiments. Models trained with spectrogram features converge slower and to worse minimum, but the difference when using CMN is not very big compared to FBanks. 
 Question: What normalization techniques are mentioned?


Output: FBanks with cepstral mean normalization (CMN) variance with mean normalization (CMVN)