CoLi at UdS at SemEval-2020 Task 12: Offensive Tweet Detection with Ensembling
We present our submission and results for SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) where we participated in offensive tweet classification tasks in English, Arabic, Greek, Turkish and Danish. Our approach included classical machine learning architectures such as support vector machines and logistic regression combined in an ensemble with a multilingual transformer-based model (XLM-R). The transformer model is trained on all languages combined in order to create a fully multilingual model which can leverage knowledge between languages. The machine learning model hyperparameters are fine-tuned and the statistically best performing ones included in the final ensemble.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningLanguage IdentificationregressionXLM-RMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
KMI-Coling at SemEval-2019 Task 6: Exploring N-grams for Offensive Language detection
In this paper, we present the system description of Offensive language detection tool which is developed by the KMI{\_}Coling under the OffensEval Shared task. The OffensEval Shared Task was conducted in SemEval 2019 wor…
nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection
This paper presents our submission for the SemEval shared task 6, sub-task A on the identification of offensive language. Our proposed model, C-BiGRU, combines a Convolutional Neural Network (CNN) with a bidirectional Re…
Language ModelingLanguage ModellingUSF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings
In this paper, we present a system description for the SemEval-2019 Task 6 submitted by our team. For the task, our system takes tweet as an input and determine if the tweet is offensive or non-offensive (Sub-task A). In…
General ClassificationLemmatizationWord EmbeddingsLaSTUS/TALN at SemEval-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based Bi-LSTM model
We present a bidirectional Long-Short Term Memory network for identifying offensive language in Twitter. Our system has been developed in the context of the SemEval 2019 Task 6 which comprises three different sub-tasks, …
Word EmbeddingsNAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets
In this paper, we present the system submitted to "SemEval-2020 Task 12". The proposed system aims at automatically identify the Offensive Language in Arabic Tweets. A machine learning based approach has been used to des…
BIG-bench Machine Learning