IITP - AINLPML at S em E val-2020 Task 12: Offensive Tweet Identification and Target Categorization in a Multitask Environment (2020)

Abstract: In this paper, we describe the participation of IITP-AINLPML team in the SemEval-2020 SharedTask 12 on Offensive Language Identification and Target Categorization in English Twitter data. Our proposed model learns to extract textual features using a BiGRU-based deep neural network supported by a Hierarchical Attention architecture to focus on the most relevant areas in the text. We leverage the effectiveness of multitask learning while building our models for sub-task A and B. We do necessary undersampling of the over-represented classes in the sub-tasks A and C.During training, we consider a threshold of 0.5 as the separation margin between the instances belonging to classes OFF and NOT in sub-task A and UNT and TIN in sub-task B. For sub-task C, the class corresponding to the maximum score among the given confidence scores of the classes(IND, GRP and OTH) is considered as the final label for an instance. Our proposed model obtains the macro F1-scores of 90.95%, 55.69% and 63.88% in sub-task A, B and C, respectively.

## introduction with the advancement of technology and gaining popularity of several social media platforms, there has been a manifold increase in the number of users active in such channels. more often than not, these users tend to misuse their power of freedom of speech and violate acceptable usage policies of most of the forums. this has necessitated detecting any offensive or obscene posts, comments, images, etc. and prevent further dissemination of the same to curtail its effect on social media. one promising solution is to develop automated tools for identification of offensive language using various natural language processing (nlp) and machine learning (ml) techniques. in the past few years, many shared tasks and seminars were introduced to address this problem and provide relevant annotated data collected from various social media. some of the related tasks are: alw 1 (related to abusive language identification), trac 1 2018 2 related to aggression identification @xcite , germeval task 2 3 , hateval 2019 4 (i orts, 2019), hasoc 2019 5 and offenseval 2019 task 6 6 related to offensive language identification @xcite . recent works have considered categorizing hate speech problem into sub-classes like abusive, aggressive, or offensive speech. such categorization of social media posts help law-enforcement agencies with the surveillance of social media. ## related work for quite some time now, researchers have studied and reported their observations and results related to online misuse of social media platforms @xcite ; @xcite ; @xcite ). misuse may take many forms like cyberbullying @xcite , trolling @xcite and offensive language @xcite . @xcite proposed a range of cnn-based deep neural models for classification of tweets into one of the following categories: sexism, racism, either (sexism or racism) and non-hate. twitter hate speech text includes racism, sexism, both and a non-hate-speech classification system. ( @xcite ) introduced a series of sub-tasks related to cyberbullying, online abuse and hate speech. ( @xcite ) addressed the differences in general profanity and hate speech. @xcite introduced the wikipedia comments corpora for building models to evaluate hate speech classifiers. usage of word n-grams and sentiment lexicons were reported in @xcite . in a comprehensive survey by @xcite , various linguistic, lexical, sentiment, surface features, etc. were identified that can be useful to build a classifier for detection of hate speech. a cnn and gru based approach was proposed by @xcite for hate speech detection. an interesting work on predicting future hostility and its intensity looking at the current situation was studied in @xcite . though most of the works related to offensive language and hate speech has been in english, still there are few works in other languages as well. @xcite worked on a large dataset of sports comments in greek and proposed several approaches to handle user content moderation using neural networks and sophisticated attention mechanism. @xcite introduced a corpus in arabic consisting of obscene and offensive user comments and words in social media. @xcite explored malpractices in social networking sites in slovenia mainly relating to the legal domain and subsequently introduced a dataset and annotation schema about such practices. @xcite proposed a system to detect and alter obscene and vulgar sentences in chinese. the germeval shared task @xcite was introduced to facilitate research on the offensive language identification in microposts in the german language. ## methodology we use pre-trained glove foot_2 @xcite word embeddings to initialize our embedding layer and further fine-tune it on our training data while learning. the output from the embedding layer is passed through a bigru (256 units) layer which encodes the input representation to hidden representation. we leverage the effectiveness of hierarchical attention (hatt) based document classification technique @xcite to attend upon the instances more precisely. the attended vector is passed through two separate task-specific fully connected layers followed by their respective output layers (2 neurons with softmax activation for the classification task and 1 neuron with sigmoid activation for the regression task). for sub-task c, we train our model on the classification task only so there is a single output layer with 3 neurons (signifying 3 classes) with softmax activation. rest of the architecture is similar to the models for sub-task a and b with an exception that the output from the attention layer is passed through a single fully connected layer before moving to the output layer. figure 1 shows the general model architecture for our experiments.