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.

Input: Consider Input: We published a survey on Reddit asking Danish speaking users to suggest offensive, sexist, and racist terms for a lexicon. Language and user behaviour varies between platforms, so the goal is to capture platform-specific terms. This gave 113 offensive and hateful terms which were used to find offensive comments. The remainder of comments in the corpus were shuffled and a subset of this corpus was then used to fill the remainder of the final dataset. The resulting dataset contains 3600 user-generated comments, 800 from Ekstra Bladet on Facebook, 1400 from r/DANMAG and 1400 from r/Denmark.

Output: How large was the dataset of Danish comments?

Input: Consider Input: In the future, we aim to use the corpus for annotation projects such as parts-of-speech tagging, named entity recognition. Furthermore, the generated word embeddings will be utilized for the automatic construction of Sindhi WordNet.

Output: Are trained word embeddings used for any other NLP task?

Input: Consider Input: BIBREF0 (BIBREF0) developed Moral Choice Machine computes the cosine similarity in a sentence embedding space of an arbitrary action embedded in question/answer pairs.
Output: What is the Moral Choice Machine?