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.
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Question: One of the major drawbacks of SCRF models is their high computational cost. In our experiments, the CTC model is around 3-4 times faster than the SRNN model that uses the same RNN encoder. To cut down the computational cost, we investigated if CTC can be used to pretrain the RNN encoder to speed up the training of the joint model. Figure 2 shows the convergence curves of the joint model with and without CTC pretraining, and we see pretraining indeed improves the convergence speed of the joint model.
Question: Can SCRF be used to pretrain the model?

Answer: Yes-no

Question: The resulting dataset consists of 22,880 users, 41,094 blogs, and 561,003 posts. Table TABREF2 presents additional statistics of our dataset.
Question: How many users do they look at?

Answer: Extractive

Question: In natural language processing (NLP), taking into account the morphological complexity inherent to each language could be important for improving or adapting the existing methods, since the amount of semantic and grammatical information encoded at the word level, may vary significantly from language to language. This information could be useful for linguistic analysis and for measuring the impact of different word form normalization tools depending of the language.
Question: what is the practical application for this paper?

Answer:
Abstractive