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:  Plackett-Luce Model for SMT Reranking
After being de-duplicated, the N-best list has an average size of around 300, and with 7491 features. This experiment displays, in large-scale features, the Plackett-Luce model correlates with BLEU score very well, and alleviates overfitting in some degree.
Example Output: What experiments with large-scale features are performed?

Example Input: Recently, MADAMIRA BIBREF6 system has been evaluated using a blind testset (25K words for Modern Standard Arabic (MSA) selected from Penn Arabic Tree bank (PATB)), and the reported accuracy was 96.2% as the percentage of words where the chosen analysis (provided by SAMA morphological analyzer BIBREF7 ) has the correct lemma. As MSA is usually written without diacritics and IR systems normally remove all diacritics from search queries and indexed data as a basic preprocessing step, so another column for undiacritized lemma is added and it's used for evaluating our lemmatizer and comparing with state-of-the-art system for lemmatization; MADAMIRA.
Example Output: What is the state of the art?

Example Input: To verify the effectiveness of our proposed model, we conduct multiple experiments on three Chinese Machine Reading Comprehension datasets, namely CMRC-2017 BIBREF17 , People's Daily (PD) and Children Fairy Tales (CFT) BIBREF2  Besides, we also use the Children's Book Test (CBT) dataset BIBREF1 to test the generalization ability in multi-lingual case.
Example Output:
which public datasets were used?