Question: What is English mixed with in the TRAC dataset?
Evidence: In future, we will explore other methods to increase the understanding of deep learning models on group targeted text, although the categories are well defined we will look after if we further fine-tune the categories with more data. 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).
The block diagram of the proposed system is shown in Figure FIGREF22. The proposed system does not use any data augmentation techniques like BIBREF14, which is the top performer in TRAC (in English code-mixed Facebook data). This means the performance achieved by our system totally depends on the training dataset provided by TRAC. This also proves the effectiveness of our approach. 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 remaining part of this paper is organized as follows: Section SECREF2 is an overview of related work. Section SECREF3 presents the methodology and algorithmic details. Section SECREF4 discusses the experimental evaluation of the system, and Section SECREF5 concludes this paper.
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.
 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.
Answer: The answers are shown as follows:
* Hindi