INGEOTEC at S em E val-2020 Task 12: Multilingual Classification of Offensive Text (2020)

Abstract: This paper describes our participation in OffensEval challenges for English, Arabic, Danish, Turkish, and Greek languages. We used several approaches, such as μTC, TextCategorization, and EvoMSA. Best results were achieved with EvoMSA, which is a multilingual and domain-independent architecture that combines the prediction from different knowledge sources to solve text classification problems.

## introduction freedom of expression is one of the important factors that make social media platforms popular; there, people interact with others and express themselves freely. however, offensive content has become pervasive in social media that could mock or insult both individuals or groups of people. thus, detecting offenses and misbehavior expressed in text form is useful to measure the people's feelings and warn them about possible attacks on others such as abusive language, hate speech, cyberbullying, trolling, among others social problems @xcite . the scientific community organize several challenges periodically, like offenseval, to tackle these text classification problems. in 2019, offenseval @xcite organized three tasks for english, and this year (offenseval-2020) @xcite are included arabic, greek, danish, and turkish languages. the tasks consist in identifying the type of offensive language in short texts. in this paper, we present the results of our participation in all tasks and all languages previously mentioned. offenseval challenge consists in determining whether a given message has offensive content. it is divided into three tasks a, b, and c for english and only task a for arabic, greek, danish, and turkish. task a is dedicated to identifying the offensive language, i.e., determine if a message is offensive or not offensive. task b is about categorizing offense types; that is, a tweet containing an insult or threat to someone, or a tweet containing non-targeted profanity and swearing. finally, task c focuses on identifying the target, i.e., whether the offensive content is about an individual, a group, or others. abusive and offensive language identification problems includes aggression @xcite , hate speech (basile et al., 2019), cyberbullying @xcite , and offenses on targets (individuals or groups) @xcite . typically, these problems are tackled as supervised learning problems, i.e., classification. for example, to identify the offensive language, the work by @xcite uses pre-trained bert with fine-tuning on the training dataset. the approach described by @xcite uses pre-trained bert and glove vectors, along with techniques for overcoming unbalanced class distribution in the provided test data, authors show how these techniques increase the performance in general. @xcite study the performance of combining tf-idf weighting, lexicon-based approaches, and support vector machines (svm). a rule-based blacklist approach is used by @xcite . for our participation in the contest, for all tasks and all languages, we use the same approach for final runs proposed by @xcite . our approach takes into account several features; for example, the effects of character-level n-grams, that is broadly studied for related tasks in the work of @xcite . in particular, text modeling is a crucial factor in our multilingual approach; therefore, we used the approach presented by @xcite that selects the best configuration on the datasets concerned. we also use external knowledge to the given training set to support the classification task; in this sense, our approach named evomsa (section 2.1) is a stacking system that focuses on sentiment analysis, and, in general, on text classification. ## system description we used our framework based on stacking generalization and genetic programming named evomsa to tackle the offenseval tasks. evomsa is composed of a stack of several classifiers among them b4msa, emospace, lexicon-based to produce predictions, later a classifier, e.g. evodag, combines the predictions into the final one. ## experimental settings as we mentioned, to determine the best configuration of parameters for text modeling, µtc (aka b4msa) integrates a hyper-parameter optimization phase that ensures the performance of the classifier based on the training data. the text modeling parameters for µtc and textcategorization are computed for each language. a text transformation feature could be binary (yes/no) or ternary (group/delete/none) option and tokenizers generate text chunks in a range of lengths, all tokens generated are part of the text representation, see the approach proposed by @xcite . ## results we present the results of our approaches for the offenseval contest in evomsa combines, using evodag, the output of different text models such as µtc (b4msa), a lexicon-based model, an emoji-space model, and fasttext. a complete study of the effects of wide range of models for evomsa, we refer the interested readers to the work presented by @xcite . ## conclusions in this paper, we presented our solution for offenseval 2020. we showed the competitiveness of our approach in both training and test phases. our systems are designed to be multilingual and language and domain-independent as much as possible. for the training step, we used extra knowledge from datasets out of any specific emotion of the contests, but categories or emotions related to sentiment-analysis information. our solution performs well in english (task a), arabic, and greek languages; however, there is room for further improvements in performance using another sort of knowledge for specific domains and languages.