Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userntust-nlp-2 at ROCLING-2021 Shared Task: BERT-based semantic analyzer with word-level information
NTUST-NLP-2 في Rocling-2021 المهمة المشتركة: محلل الدلالي القائم على بيرت مع معلومات مستوى الكلمات
516
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في هذه الورقة، اقترحنا محلل دلالي أبعاد بر فندقية، وهو مصمم من خلال دمج معلومات على مستوى Word.حقق نموذجنا ثلاثة من أفضل النتائج في أربعة مقاييس على rocling 2021 المهمة المشتركة: تحليل المعنويات الأبعاد للنصوص التعليمية ".أجرينا سلسلة من التجارب لمقارنة فعالية مختلف الأساليب المدربة مسبقا.علاوة على ذلك، فإن النتائج تعاني أيضا على أن طريقتنا يمكن أن تحسن بشكل كبير من الأداء من الأساليب الكلاسيكية.استنادا إلى التجارب، ناقشنا أيضا تأثير هياكنات النموذج ومجموعات البيانات.
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 Analysis 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.
References used
https://aclanthology.org/
rate research
Read More
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.
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.
This paper presents the GX system for the Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) task. The purpose of the MCL-WiC task is to tackle the challenge of capturing the polysemous nature of words without relying on a fixed
sense inventory in a multilingual and cross-lingual setting. To solve the problems, we use context-specific word embeddings from BERT to eliminate the ambiguity between words in different contexts. For languages without an available training corpus, such as Chinese, we use neuron machine translation model to translate the English data released by the organizers to obtain available pseudo-data. In this paper, we apply our system to the English and Chinese multilingual setting and the experimental results show that our method has certain advantages.
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.
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.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
