In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Example Input: We assume that speech transcripts can be extracted from audio signals with high accuracy, given the advancement of ASR technologies BIBREF7 .
Example Output: Do they use datasets with transcribed text or do they determine text from the audio?

Example Input: To create training data for unanswerable question generation, we use (plausible) answer spans in paragraphs as pivots to align pairs of answerable questions and unanswerable questions. In this way, we obtain the data with which the models can learn to ask unanswerable questions by editing answerable ones with word exchanges, negations, etc.
Example Output: How do they ensure the generated questions are unanswerable?

Example 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.
Example Output:
How are the two different models trained?