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SemEval 2020 Task 12 focusses on the challenge of detecting offensive language present in social media. To tackle the subtask A (Offensive language identification) and the subtask C (Identifying the targets of offensive language) of SemEval 2020 Task 12, we developed ensembles of classifiers based on DistilBERT pre-trained language model. Our ensemble models achieved F1 scores of 0.906 for the subtask A, and 0.623 for the subtask C


COLING 2020

Ensembles of Classifiers for Offensive Language Detection in Social Media

cyberbullying detection

ensemble models

offensive language detection

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

poster

This work presents a system based on BERT for the offensive language detection in English and MBERT for the multilingual analysis. We proposed the system for the three subtasks of the task 12 of SemEval-2020: OffensEval 2020.

BERT for Multilingual Offensive Language Detection

This work presents a multimodal model that combines the Bert and VGG architectures for the analysis of the textual and visual information from memes respectively. We proposed the model for each of the three subtasks of the task 8 of SemEval-2020: Memotion Analysis. The objective is to identify whether an Internet meme presents one, several or none of the categories sarcasm, humor, offense and motivation. Moreover, the sentiment analysis of memes is included.

Study of Multimodal Techniques for Memes Analysis

Code mixing is a common phenomena in multilingual societies where people switch from one language to another for various reasons. Recent advances in public communication over different social media sites have led to an increase in the frequency of code-mixed use in written language. In this paper, we present the Generative Morphemes with Attention (GenMA) Model sentiment analysis system, which contributes to the SemEval 2020 Task 9 SentiMix. This system aims to predict the sentiments of the given English-Hindi code-mixed tweets without using word-level language tags, but instead inferring emotion content automatically using a morphological model. The system is based on a novel deep neural network (DNN) architecture, which has outperformed the baseline F1-score on the test data-set as well as the validation data-set.

Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text

This paper describes the participation of SINAI team at Task 12: OffensEval 2: Multilingual Offensive Language Identification in Social Media. In particular, the participation in Sub-task A in English which consists of identifying tweets as offensive or not offensive. We preprocess the dataset according to the language characteristics used on social media. Then, we select a small set from the training set provided by the organizers and fine-tune different Transformer-based models in order to test their effectiveness. Our team ranks 20th out of 85 participants in Subtask-A using the XLNet model.

Offensive language identification exploring transfer learning models

This work addresses the classification problem defined by sub-task A (English only) of the OffensEval 2020 challenge. We used a semi-supervised approach to classify given tweets into an offensive (OFF) or not-offensive (NOT) class. As the OffensEval 2020 dataset is loosely labelled with confidence scores given by unsupervised models, we used last year's offensive language identification dataset (OLID) to label the OffensEval 2020 dataset. Our approach uses a pseudo-labelling method to annotate the current dataset. We trained four text classifiers on the OLID dataset and the classifier with the highest macro-averaged F1-score has been used to pseudo label the OffensEval 2020 dataset. The same model which performed best amongst four text classifiers on OLID dataset has been trained on the combined dataset of OLID and pseudo labelled OffensEval 2020. We evaluated the classifiers with precision, recall and macro-averaged F1-score as the primary evaluation metric on the OLID and OffensEval 2020 datasets.

Pseudo labelling for offensive content classification

This paper describes the model proposed and submitted by our RIJP team to SemEval 2020Task1: Unsupervised Lexical Semantic Change Detection. In the model, words are represented by Gaussian distributions. For Subtask 1, the model achieved average scores of 0.51 and 0.70 in the evaluation and post-evaluation processes, respectively. The higher score in the post-evaluation process than that in the evaluation process was achieved owing to appropriate parameter tuning. The results indicate that the proposed Gaussian-based embedding model is able to express semantic shifts while having a low computational complexity.

Gaussian-based Embeddings for Semantic Change Detection

This paper describes our system for SemEval-2020 Task 4: Commonsense Validation and Explanation (Wang et al., 2020). We propose a novel Knowledge-enhanced Graph Attention Network (KEGAT) architecture for this task, leveraging heterogeneous knowledge from both the structured knowledge base (i.e. ConceptNet) and unstructured text to better improve the ability of a machine in commonsense understanding. This model has a powerful commonsense inference capability via utilizing suitable commonsense incorporation methods and upgraded data augmentation techniques. Besides, an internal sharing mechanism is cooperated to prohibit our model from insufficient and excessive reasoning for commonsense. As a result, this model performs quite well in both validation and explanation. For instance, it achieves state-of-the-art accuracy in the subtask called Commonsense Explanation (Multi-Choice). We officially name the system as ECNU-SenseMaker. Code is publicly available at https://github.com/ECNU-ICA/ECNU-SenseMaker.

Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation

This paper describes our contribution to SemEval-2020 Task 7: Assessing Humor in Edited News
Headlines. Here we present a method based on a deep neural network. In recent years, quite some
attention has been devoted to humor production and perception. Our team KDEhumor employs
recurrent neural network models including Bi-Directional LSTMs (BiLSTMs). Moreover, we
utilize the state-of-the-art pre-trained sentence embedding techniques. We analyze the performance
of our method and demonstrate the contribution of each component of our architecture.

A Neural Network Model for Detecting Funniness in Dataset Humicroedit


SemEval Task 4 Commonsense Validation and Explanation Challenge is to validate whether a system can differentiate natural language statements that make sense from those that do not make sense. Two subtasks, A and B, are focused in this work, i.e., detecting against-common-sense statements and selecting explanations of why they are false from the given options. Intuitively, commonsense validation requires additional knowledge beyond the given statements. Therefore, we propose a system utilising pre-trained sentence transformer models based on BERT, RoBERTa and DistillBERT architectures to embed the statements before classification. According to the results, these embeddings can improve the performance of the typical MLP and LSTM classifiers as downstream models of both subtasks compared to regular tokenised statements. These embedded statements are shown to comprise additional information from external resources which help validate common sense in natural language.

Pre-trained Sentence Transformer Models for Commonsense Validation and Explanation

we describe our mUlti-task learNIng for cOmmonsense reasoNing (UNION) system submitted for Task C of the SemEval2020 Task 4, which is to generate a reason explaining why a given false statement is non-sensical. However, we found in the early experiments that simple adaptations such as fine-tuning GPT2 often yield dull and non-informative generations (e.g. simple negations). In order to generate more meaningful explanations, we propose UNION, a unified end-to-end framework, to utilize several existing commonsense datasets so that it allows a model to learn more dynamics under the scope of commonsense reasoning.

Ensembles of Classifiers for Offensive Language Detection in Social Media

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BERT for Multilingual Offensive Language Detection

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Next from COLING 2020

BERT for Multilingual Offensive Language Detection

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