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Register a new userFHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning
FHAC في Germeval 2021: تحديد التعليقات الألمانية السامة والمشاركة، والحقائق مع التعلم الفرعي
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أدى توافر تمثيلات اللغة التي تعلمتها نماذج الشبكة العصبية العصبية الكبيرة (مثل Bert and Electra) إلى تحسينات في العديد من مهام معالجة اللغة الطبيعية المصب في السنوات الأخيرة.تختلف النماذج المحددة عادة في الأهداف المحددة، والبنية، ومجموعات البيانات التي تم تدريبها عليها والتي يمكن أن تؤثر على أداء المصب.في هذه المساهمة، نحن نضرب نماذج بيرت الألمانية والألمانية الكترا لتحديد السامة (الفرعية 1)، وجذابة (SubTask 2)، وتعليقات تدعي الحقائق (SubTask 3) في بيانات Facebook المقدمة من مسابقة Germeval 2021.أنشأنا مجموعة من هذه النماذج والتحقيق في ما إذا كان أداء التصنيف يعتمد على عدد أعضاء الفرقة وتكوينهم.على بيانات خارج العينة، حققت أفضل مجموعة لدينا درجة ماكرو F1 من 0.73 (لجميع المهام الفرعية)، وعشرات F1 من 0.72، 0.70، و 0.76 للحصول على المهام الفرعية 1، 2، و 3، على التوالي.
The availability of language representations learned by large pretrained neural network models (such as BERT and ELECTRA) has led to improvements in many downstream Natural Language Processing tasks in recent years. Pretrained models usually differ in pretraining objectives, architectures, and datasets they are trained on which can affect downstream performance. In this contribution, we fine-tuned German BERT and German ELECTRA models to identify toxic (subtask 1), engaging (subtask 2), and fact-claiming comments (subtask 3) in Facebook data provided by the GermEval 2021 competition. We created ensembles of these models and investigated whether and how classification performance depends on the number of ensemble members and their composition. On out-of-sample data, our best ensemble achieved a macro-F1 score of 0.73 (for all subtasks), and F1 scores of 0.72, 0.70, and 0.76 for subtasks 1, 2, and 3, respectively.
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In this paper, we report on our approach to addressing the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments for the German language. We submitted three runs for each subtask based on ensembles of three mo
dels each using contextual embeddings from pre-trained language models using SVM and neural-network-based classifiers. We include language-specific as well as language-agnostic language models -- both with and without fine-tuning. We observe that for the runs we submitted that the SVM models overfitted the training data and this affected the aggregation method (simple majority voting) of the ensembles. The model records a lower performance on the test set than on the training set. Exploring the issue of overfitting we uncovered that due to a bug in the pipeline the runs we submitted had not been trained on the full set but only on a small training set. Therefore in this paper we also include the results we get when trained on the full training set which demonstrate the power of ensembles.
We present the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments. This shared task comprises three binary classification subtasks with the goal to identify: toxic comments, engaging comments, and comments
that include indications of a need for fact-checking, here referred to as fact-claiming comments. Building on the two previous GermEval shared tasks on the identification of offensive language in 2018 and 2019, we extend this year's task definition to meet the demand of moderators and community managers to also highlight comments that foster respectful communication, encourage in-depth discussions, and check facts that lines of arguments rely on. The dataset comprises 4,188 posts extracted from the Facebook page of a German political talk show of a national public television broadcaster. A theoretical framework and additional reliability tests during the data annotation process ensure particularly high data quality. The shared task had 15 participating teams submitting 31 runs for the subtask on toxic comments, 25 runs for the subtask on engaging comments, and 31 for the subtask on fact-claiming comments. The shared task website can be found at https://germeval2021toxic.github.io/SharedTask/.
This paper addresses the identification of toxic, engaging, and fact-claiming comments on social media. We used the dataset made available by the organizers of the GermEval2021 shared task containing over 3,000 manually annotated Facebook comments in
German. Considering the relatedness of the three tasks, we approached the problem using large pre-trained transformer models and multitask learning. Our results indicate that multitask learning achieves performance superior to the more common single task learning approach in all three tasks. We submit our best systems to GermEval-2021 under the team name WLV-RIT.
In this paper we present UPAppliedCL's contribution to the GermEval 2021 Shared Task. In particular, we participated in Subtasks 2 (Engaging Comment Classification) and 3 (Fact-Claiming Comment Classification). While acceptable results can be obtaine
d by using unigrams or linguistic features in combination with traditional machine learning models, we show that for both tasks transformer models trained on fine-tuned BERT embeddings yield best results.
In this paper we investigate the efficacy of using contextual embeddings from multilingual BERT and German BERT in identifying fact-claiming comments in German on social media. Additionally, we examine the impact of formulating the classification pro
blem as a multi-task learning problem, where the model identifies toxicity and engagement of the comment in addition to identifying whether it is fact-claiming. We provide a thorough comparison of the two BERT based models compared with a logistic regression baseline and show that German BERT features trained using a multi-task objective achieves the best F1 score on the test set. This work was done as part of a submission to GermEval 2021 shared task on the identification of fact-claiming comments.
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