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: We first evaluate our converted embedding models on multi-language lexical similarity and relatedness tasks, as a sanity check, to make sure the word sense induction process did not hurt the general performance of the embeddings. Then, we test the sense embeddings on WSD task.
Example Output: Was any extrinsic evaluation carried out?

Example Input: In order to derive the discourse features, an entity grid is constructed by feeding the document through an NLP pipeline to identify salient entities. Two flavors of discourse features are created by populating the entity grid with either (i) grammatical relations (GR) or (ii) RST discourse relations (RST).  Denoting a feature as such with INLINEFORM4 , we construct the pooling vector INLINEFORM5 for the char-bigrams, and concatenate INLINEFORM6 to INLINEFORM7 before feeding the resulting vector to the softmax layer.
Example Output: How are discourse features incorporated into the model?

Example Input: In sub-task C the goal is to classify the target of the offensive language. Only posts labeled as targeted insults (TIN) in sub-task B are considered in this task BIBREF17 . Samples are annotated with one of the following:

Individual (IND): Posts targeting a named or unnamed person that is part of the conversation. In English this could be a post such as @USER Is a FRAUD Female @USER group paid for and organized by @USER. In Danish this could be a post such as USER du er sku da syg i hoved. These examples further demonstrate that this category captures the characteristics of cyberbullying, as it is defined in section "Background" .

Group (GRP): Posts targeting a group of people based on ethnicity, gender or sexual orientation, political affiliation, religious belief, or other characteristics. In English this could be a post such as #Antifa are mentally unstable cowards, pretending to be relevant. In Danish this could be e.g. Åh nej! Svensk lorteret!

Other (OTH): The target of the offensive language does not fit the criteria of either of the previous two categories. BIBREF17 . In English this could be a post such as And these entertainment agencies just gonna have to be an ass about it.. In Danish this could be a post such as Netto er jo et tempel over lort.
Example Output:
How many categories of offensive language were there?