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: We make copies of the monolingual model for each language and add additional crosslingual latent variables (CLVs) to couple the monolingual models, capturing crosslingual semantic role patterns. Concretely, when training on parallel sentences, whenever the head words of the arguments are aligned, we add a CLV as a parent of the two corresponding role variables.  
 Question: Which additional latent variables are used in the model?

Answer: Extractive


Question: CoVoST is currently built on that release and covers the following 11 languages: French, German, Dutch, Russian, Spanish, Italian, Turkish, Persian, Swedish, Mongolian and Chinese. 
 Question: Is Arabic one of the 11 languages in CoVost?

Answer: Yes-no


Question: We manually investigate the errors made by artificial neural networks for morphological inflection in a target language after pretraining on different source languages. For our qualitative analysis, we make use of the validation set. Therefore, we show validation set accuracies in Table TABREF19 for comparison. We manually annotate the outputs for the first 75 development examples for each source-target language combination. All found errors are categorized as belonging to one of the following categories. 
 Question: How is the performance on the task evaluated?

Answer:
Abstractive