In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

The challenge is addressed as follows: given a natural language input sequence describing the scene, such as a piece of a story coming from a transcript, the goal is to infer which action is most likely to happen next. 
 Question: Do they literally just treat this as "predict the next spell that appears in the text"?
Yes-no

The National Gang Threat Assessment Report confirms that at least tens of thousands of gang members are using social networking websites such as Twitter and video sharing websites such as YouTube in their daily life BIBREF0 . They are very active online; the 2007 National Assessment Center's survey of gang members found that 25% of individuals in gangs use the Internet for at least 4 hours a week BIBREF4 . 
 Question: Do the authors provide evidence that 'most' street gang members use Twitter to intimidate others?
Yes-no

In order to achieve these configuration the TIMIT data was split. Fig. FIGREF12 illustrates the split of the data into 8 subsets (A-H). The TIMIT dataset contains speech from 462 speakers in training and 168 speakers in the test set, with 8 utterances for each speaker. The TIMIT training and test set are split into 8 blocks, where each block contains 2 utterances per speaker, randomly chosen. Thus each block A,B,C,D contains data from 462 speakers with 924 utterances taken from the training sets, and each block E,F,G,H contains speech from 168 test set speakers with 336 utterances.

For Task a training of embeddings and the classifier is identical, namely consisting of data from blocks (A+B+C+E+F+G). The test data is the remainder, namely blocks (D+H). For Task b the training of embeddings and classifiers uses (A+B+E+F) and (C+G) respectively, while again using (D+H) for test. Task c keeps both separate: embeddings are trained on (A+B+C+D), classifiers on (E+G) and tests are conducted on (F+H). Note that H is part of all tasks, and that Task c is considerably easier as the number of speakers to separate is only 168, although training conditions are more difficult. 
 Question: What TIMIT datasets are used for testing?

Abstractive