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: To test if the learned representations can separate phonetic categories, we use a minimal pair ABX discrimination task BIBREF19 , BIBREF20 . For the within-speaker task, all the phones triplets belong to the same speaker (e.g. $x$3 ) Finally the scores for every pair of central phones are averaged and subtracted from 1 to yield the reported within-talker ABX error rate. For the across-speaker task, $x$4 and $x$5 belong to the same speaker, and $x$6 to a different one (e.g. $x$7 ). The scores for a given minimal pair are first averaged across all of the pairs of speakers for which this contrast can be made. As above, the resulting scores are averaged over all contexts over all pairs of central phones and converted to an error rate.
Example Output: What is the metric that is measures in this paper?

Example Input: In this work, we explore the applicability of our method to three popular architectures of this layer: the LSTM-based, the CNN-based, and the transformer-based. Table TABREF46 shows performance of our method with different sequence modeling architectures. From the table, we can first see that the LSTM-based architecture performed better than the CNN- and transformer- based architectures.
Example Output:
Which are the sequence model architectures this method can be transferred across?