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]: The base sentence embedding model is a BiLSTM over all words in the respective sequences with randomly initialised word embeddings, following BIBREF30 . We opt for this strong baseline sentence encoding model, as opposed to engineering sentence embeddings that work particularly well for this dataset, to showcase the dataset. We would expect pre-trained contextual encoding models, e.g. ELMO BIBREF33 , ULMFit BIBREF34 , BERT BIBREF35 , to offer complementary performance gains, as has been shown for a few recent papers BIBREF36 , BIBREF37 .
Question: What were the baselines?
[EX A]: a BiLSTM over all words in the respective sequences with randomly initialised word embeddings, following BIBREF30

[EX Q]: Despite these shortcomings in using WER as a metric of success, the WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD based on the given reading passage and answer. WER can be used for initial analyses that can lead to deeper insights as discussed further below.
Question: Why did they choose WER as evaluation metric?
[EX A]: WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD WER can be used for initial analyses

[EX Q]: During the final training process, 500 validation data is used to generate the recurrent neural model, which is 3% of the training data. After finishing the training, the accuracy of the generated model using validation data from the source corpus was 74.40%
Question: What dataset is used to measure accuracy?
[EX A]:
validation data