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.

Q: We present here a step in this direction: a probabilistic semantic parser that uses a large knowledge base (NELL) to form a prior probability distribution on the meanings of sentences it parses, and that "understands" each sentence either by identifying its existing beliefs that correspond to the sentence's meaning, or by creating new beliefs.

A: What knowledge bases do they use?
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Q: Experimental results show that our joint model outperforms the visual-only model in all cases, and the text-only model on Wikipedia and two subsets of arXiv.

A: Do the methods that work best on academic papers also work best on Wikipedia?
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Q:  In the future, we are planning to pay attention on a generalized language model for code-mixed texts which can also handle Hindi-code-mixed and other multi-lingual code-mixed datasets (i.e., trying to reduce the dependencies on language-specific code-mixed resources). Our system outperforms all the previous state of the art approaches used for aggression identification on English code-mixed TRAC data, while being trained only from Facebook comments the system outperforms other approaches on the additional Twitter test set. The fine-grained definition of the aggressiveness/aggression identification is provided by the organizers of TRAC-2018 BIBREF0, BIBREF2. They have classified the aggressiveness into three labels (Overtly aggressive(OAG), Covertly aggressive(CAG), Non-aggressive(NAG)). The detailed description for each of the three labels is described as follows:

Overtly Aggressive(OAG) - This type of aggression shows direct verbal attack pointing to the particular individual or group. For example, "Well said sonu..you have courage to stand against dadagiri of Muslims".

Covertly Aggressive(CAG) - This type of aggression the attack is not direct but hidden, subtle and more indirect while being stated politely most of the times. For example, "Dear India, stop playing with the emotions of your people for votes."

Non-Aggressive(NAG) - Generally these type of text lack any kind of aggression it is basically used to state facts, wishing on occasions and polite and supportive.

A:
What is English mixed with in the TRAC dataset?
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