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Register a new userHunterSpeechLab at GermEval 2021: Does Your Comment Claim A Fact? Contextualized Embeddings for German Fact-Claiming Comment Classification
Hunterspeechlab في Germeval 2021: هل يدعي تعليقك حقيقة؟تضمينات السياق لتصنيف تعليق الحقائق الألمانية
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في هذه الورقة، نحقق في فعالية استخدام المدينات السياقية من بيرت متعددة اللغات، بيرت الألمانية في تحديد تعليقات المطالبة بالحقائق باللغة الألمانية على وسائل التواصل الاجتماعي.بالإضافة إلى ذلك، ندرس تأثير صياغة مشكلة التصنيف كأداة تعليمية متعددة المهام، حيث يحدد النموذج السمية ومشاركة التعليق بالإضافة إلى تحديد ما إذا كان يدعي الحقيقة.نحن نقدم مقارنة شاملة من النماذج التي تستند إلى بيرت مقارنة بناسي الانحدار اللوجستي وإظهار أن ميزات بيرت الألمانية المدربة باستخدام هدف متعدد المهام يحقق أفضل درجة F1 في مجموعة الاختبار.تم تنفيذ هذا العمل كجزء من تقديم المهمة المشتركة ل Germeval 2021 بشأن تحديد تعليقات المطالبة بالحقائق.
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 problem 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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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 i
n 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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We report on our submission to Task 1 of the GermEval 2021 challenge -- toxic comment classification. We investigate different ways of bolstering scarce training data to improve off-the-shelf model performance on a toxic comment classification task.
To help address the limitations of a small dataset, we use data synthetically generated by a German GPT-2 model. The use of synthetic data has only recently been taking off as a possible solution to ad- dressing training data sparseness in NLP, and initial results are promising. However, our model did not see measurable improvement through the use of synthetic data. We discuss possible reasons for this finding and explore future works in the field.
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.
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