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.

We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets. To create a larger discrepancy between the tasks, so that there is a clear dataset size imbalance, the En-De data is artificially restricted to only 1 million parallel sentences, while the full En-Fr dataset, comprising almost 40 million parallel sentences, is used entirely.  
 Question: What datasets are used for experiments?
the WMT'14 English-French (En-Fr) and English-German (En-De) datasets.

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?
Yes

The baselines are: (a) trained on S-SQuAD, (b) trained on T-SQuAD and then fine-tuned on S-SQuAD, and (c) previous best model trained on S-SQuAD BIBREF5 by using Dr.QA BIBREF20 . The proposed approach (row (f)) outperforms previous best model (row (c)) by 2% EM score and over 1.5% F1 score. 
 Question: What was the previous best model?

(c) previous best model trained on S-SQuAD BIBREF5 by using Dr.QA BIBREF20