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:
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 Output:
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 Input:
Overall, this resulted in a total of 34,432 user conversations. Together, these users gave Gunrock an average rating of 3.65 (median: 4.0), which was elicited at the end of the conversation ("On a scale from 1 to 5 stars, how do you feel about talking to this socialbot again?").
Question: What is the sample size of people used to measure user satisfaction?

Ex Output:
34,432 user conversations

Ex Input:
We use the additional INLINEFORM0 training articles labeled by publisher as an unsupervised data set to further train the BERT model. We first investigate the impact of pre-training on BERT-BASE's performance. On the same computer, fine tuning the model on the small training set took only about 35 minutes for sequence length 100.
Question: How are the two different models trained?

Ex Output:
They pre-train the models using 600000 articles as an unsupervised dataset and then fine-tune the models on small training set.