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: Word embeddings have risen in popularity for NLP applications due to the success of models designed specifically for the big data setting. Note that "big" datasets are not always available, particularly in computational social science NLP applications, where the data of interest are often not obtained from large scale sources such as the internet and social media, but from sources such as press releases BIBREF11 , academic journals BIBREF10 , books BIBREF12 , and transcripts of recorded speech BIBREF13 , BIBREF14 , BIBREF15 have proposed a model-based method for training interpretable corpus-specific word embeddings for computational social science, using mixed membership representations, Metropolis-Hastings-Walker sampling, and NCE. Experimental results for prediction, supervised learning, and case studies on state of the Union addresses and NIPS articles, indicate that high-quality embeddings and topics can be obtained using the method. The results highlight the fact that big data is not always best, as domain-specific data can be very valuable, even when it is small.
Example Output: Why is big data not appropriate for this task?

Example Input: Traditional text-to-speech (TTS) systems are composed of complex pipelines BIBREF0 , these often include accoustic frontends, duration model, acoustic prediction model and vocoder models The open source LJSpeech Dataset was used to train our TTS model. The architecture of our model utilizes RNN-based Seq2Seq model for generating mel spectrogram from text
Example Output: Which dataset(s) do they evaluate on?

Example Input: The FBFans dataset contains data from anti-nuclear-power Chinese Facebook fan groups from September 2013 to August 2014, including posts and their author and liker IDs.
Example Output:
What topic is covered in the Chinese Facebook data?