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.

Q: For DQA four participants answered each question, therefore we took the average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values over the four evaluators as the result per question. The detailed answers by the participants and available online. To assess the correctness of the answers given both by participants in the DQA experiments, and by the QALD system, we use the classic information retrieval metrics of precision (P), recall (R), and F1. INLINEFORM0 measures the fraction of relevant (correct) answer (items) given versus all answers (answer items) given. 
 Question: Do they test performance of their approaches using human judgements?

A: Yes
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Q: We use Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10 as baselines and compare the results with our different fine-tuning strategies using pre-trained BERTbase model.  
 Question: What baseline is used?

A: Waseem and Hovy BIBREF5 Davidson et al. BIBREF9 Waseem et al. BIBREF10
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Q: We have training and testing sets in three different languages: English, Chinese and Korean. When fine-tuning, we simply adopted the official training script of BERT, with default hyperparameters, to fine-tune each model until training loss converged. 
 Question: what does the model learn in zero-shot setting?

A:
we simply adopted the official training script of BERT, with default hyperparameters, to fine-tune each model until training loss converged
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