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:
The reviews we used have been extracted from TripAdvisor and originally proposed in BIBREF10 , BIBREF11 .  
 Question: Where are the hotel reviews from?

Ex Output:
TripAdvisor


Ex Input:
To compare the conversions between USVC and PitchNet, we employed an automatic evaluation score and a human evaluation score. The automatic score roughly followed the design in BIBREF13. The pitch tracker of librosa package BIBREF18 was employed to extract pitch information of the input and output audio. Then the output pitch was compared to the input pitch using the normalized cross correlation (NCC) which would give a score between 0 and 1. The higher the score is, the better the output pitch matches the input pitch. We conducted the evaluation on USVC (our) and PitchNet. The evaluated automatic scores on conversion and reconstruction tasks are shown in Tab. TABREF14. Our method performed better both on conversion and reconstruction. The scores of reconstruction are higher than conversion since both models were trained using a reconstruction loss. However, the score of our method on conversion is even higher than the score of USVC (Our) on reconstruction. Mean Opinion Score (MOS) was used as a subjective metric to evaluate the quality of the converted audio. Two questions were asked: (1) what is quality of the audio? (naturalness) (2) How well does the converted version match the original? (similarity) A score of 1-5 would be given to answer the questions. The evaluation was conducted on USVC (Our) and PitchNet. Besides, the converted samples provided by BIBREF0 was also included to give a more convincing evaluation. As shown by Tab. TABREF15, the naturalness and similarity of our method are both higher than the other two ones. Our implementation of USVC performed slightly lower than the original author's because we cannot fully reproduce the results of them. Next we qualitatively analyze the influence of input pitch in our method. We used different pitch as input to observe how the output pitch would change along with the input pitch. The input pitch was multiplied by 0.7, 1.0 and 1.3 respectively. And the output pitch was also extracted by the pitch tracker of the librosa package. Fig. FIGREF16 plots the pitch of input audio and output audio with different pitch as input while keeping the target singer the same. As shown by Fig. FIGREF16, the output pitch changes significantly along with the input pitch. The examples are also presented at our website. 
 Question: How is the quality of singing voice measured?

Ex Output:

To compare the conversions between USVC and PitchNet, we employed an automatic evaluation score and a human evaluation score.