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Register a new userROCLING-2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts
Rocling-2021 المهمة المشتركة: تحليل المعنويات الأبعاد للنصوص التعليمية
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تقدم هذه الورقة المهمة المشتركة 2021 على تحليل المشاعر الأبعاد للنصوص التعليمية التي تسعى إلى تحديد درجة المعنويات ذات القيمة الحقيقية لتعليقات التقييم الذاتي كتبها الطلاب الصينيين في كل من التكافؤ والأبعاد الإثراية.يمثل Valence درجة المشاعر اللطيفة وغير السارة (أو الإيجابية والسلبية)، وتمثل الإثريات درجة الإثارة والهدوء.من بين 7 فرق مسجلة لهذه المهمة المشتركة لتحليل المعنويات ثنائي الأبعاد، 6 نتائج مقدمة.نتوقع أن تنتج حملة التقييم هذه تقنيات تحليل المعنويات الأبعاد أكثر تقدما للمجال التعليمي.يتم إجراء جميع مجموعات البيانات مع معايير الذهب وتسجيل البرنامج النصي متاحا للباحثين.
This paper presents the ROCLING 2021 shared task on dimensional sentiment analysis for educational texts which seeks to identify a real-value sentiment score of self-evaluation comments written by Chinese students in the both valence and arousal dimensions. Valence represents the degree of pleasant and unpleasant (or positive and negative) feelings, and arousal represents the degree of excitement and calm. Of the 7 teams registered for this shared task for two-dimensional sentiment analysis, 6 submitted results. We expected that this evaluation campaign could produce more advanced dimensional sentiment analysis techniques for the educational domain. All data sets with gold standards and scoring script are made publicly available to researchers.
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We use the MacBERT transformers and fine-tune them to ROCLING-2021 shared tasks using the CVAT and CVAS data. We compare the performance of MacBERT with the other two transformers BERT and RoBERTa in the valence and arousal dimensions, respectively.
MAE and correlation coefficient (r) were used as evaluation metrics. On ROCLING-2021 test set, our used MacBERT model achieves 0.611 of MAE and 0.904 of r in the valence dimensions; and 0.938 of MAE and 0.549 of r in the arousal dimension.
This technical report aims at the ROCLING 2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts. In order to predict the affective states of Chinese educational texts, we present a practical framework by employing pre-trained languag
e models, such as BERT and MacBERT. Several valuable observations and analyses can be drawn from a series of experiments. From the results, we find that MacBERT-based methods can deliver better results than BERT-based methods on the verification set. Therefore, we average the prediction results of several models obtained using different settings as the final output.
In this shared task, this paper proposes a method to combine the BERT-based word vector model and the LSTM prediction model to predict the Valence and Arousal values in the text. Among them, the BERT-based word vector is 768-dimensional, and each wor
d vector in the sentence is sequentially fed to the LSTM model for prediction. The experimental results show that the performance of our proposed method is better than the results of the Lasso Regression model.
This paper present a description for the ROCLING 2021 shared task in dimensional sentiment analysis for educational texts. We submitted two runs in the final test. Both runs use the standard regression model. The Run1 uses Chinese version of BERT as
the base, and in Run2 we use the early version of MacBERT that Chinese version of RoBERTa-like BERT model, RoBERTa-wwm-ext. Using powerful pre-training model of BERT for text embedding to help train the model.
In this paper, we proposed a BERT-based dimensional semantic analyzer, which is designed by incorporating with word-level information. Our model achieved three of the best results in four metrics on ROCLING 2021 Shared Task: Dimensional Sentiment Ana
lysis for Educational Texts''. We conducted a series of experiments to compare the effectiveness of different pre-trained methods. Besides, the results also proofed that our method can significantly improve the performances than classic methods. Based on the experiments, we also discussed the impact of model architectures and datasets.
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