In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Input: Consider Input: After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset.
Question: How many sentence transformations on average are available per unique sentence in dataset?

Output: 27.41 transformation on average of single seed sentence is available in dataset.

Input: Consider Input: We introduce our proposed diversity, density, and homogeneity metrics with their detailed formulations and key intuitions.
Question: Did they propose other metrics?

Output: Yes

Input: Consider Input: In order to make the corpus collection easier and faster, we adopted a semi-automatic procedure based on sequential neural models BIBREF19, BIBREF20. Instead, since tokens are transcribed at morpheme level, we split Arabish tokens into characters, and Arabic tokens into morphemes, and we treated each token itself as a sequence. Our model learns thus to map Arabish characters into Arabic morphemes. With this model we automatically transcribed into Arabic morphemes, roughly, 5,000 additional tokens, corresponding to the second annotation block. Manual transcription plus a
Question: How does the semi-automatic construction process work?
Output: Automatic transcription of 5000 tokens through sequential neural models trained on the annotated part of the corpus