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 Q]: Despite the focus on sharing datasets and source codes on popular software development platforms such as GitHub (github.com) or Zenodo (zenodo.org), it is still a challenge to use data or code from other groups. 
 Question: Are datasets publicly available?
[EX A]: Yes

[EX Q]: after a shared CLWE space has been induced, the task is to retrieve target language translations for a test set of source language words retrieve target language translations for a test set of source language words BLI is cast as a ranking task in our BLI setup with only one correct translation for each “query” word, MAP is equal to mean reciprocal rank (MRR) 
 Question: How does BLI measure alignment quality?
[EX A]: we use mean average precision (MAP) as the main evaluation metric

[EX Q]: DrQA is a CRC baseline coming with the CoQA dataset. Note that this implementation of DrQA is different from DrQA for SQuAD BIBREF8 in that it is modified to support answering no answer questions by having a special token unknown at the end of the document.  DrQA+CoQA is the above baseline pre-tuned on CoQA dataset and then fine-tuned on $(\text{RC})_2$ .  BERT is the vanilla BERT model directly fine-tuned on $(\text{RC})_2$ . We use this baseline for ablation study on the effectiveness of pre-tuning. BERT+review first tunes BERT on domain reviews using the same objectives as BERT pre-training and then fine-tunes on $(\text{RC})_2$ . We use this baseline to show that a simple domain-adaptation of BERT is not good. BERT+CoQA first fine-tunes BERT on the supervised CoQA data and then fine-tunes on $(\text{RC})_2$ . We use this baseline to show that pre-tuning is very competitive even compared with models trained from large-scale supervised data. 
 Question: What is the baseline model used?
[EX A]:
The baseline models used are DrQA modified to support answering no answer questions, DrQA+CoQA which is pre-tuned on CoQA dataset, vanilla BERT, BERT+review tuned on domain reviews, BERT+CoQA tuned on the supervised CoQA data