TY - GEN
T1 - Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning
AU - Tseng, Hsiao Wen
AU - Chang, Chia Hui
AU - Chuang, Hsiu Min
N1 - Publisher Copyright:
© 2021 ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. All rights reserved.
PY - 2021
Y1 - 2021
N2 - When we are interested in a certain domain, we can collect and analyze data from the Internet. The newly collected data is not labeled, so the use of labeled data is hoped to be helpful to the new data. We perform name entity recognition (NER) and aspect-based sentiment analysis (ABSA) in multi-task learning, and combine parameter generation network (Jia et al., 2019) and DANN architecture (Ganin and Lempitsky, 2015) to build the model. In the NER task, the data is labeled with Tie, Break, and the task weight is adjusted according to the loss change rate of each task using Dynamic Weight Average (DWA) (Liu et al., 2019). This study used two different source domain data sets. The experimental results show that Tie, Break can improve the results of the model: DWA can have better performance in the results; the combination of parameter generation network and gradient reversal layer can be used for every good learning in different domain.
AB - When we are interested in a certain domain, we can collect and analyze data from the Internet. The newly collected data is not labeled, so the use of labeled data is hoped to be helpful to the new data. We perform name entity recognition (NER) and aspect-based sentiment analysis (ABSA) in multi-task learning, and combine parameter generation network (Jia et al., 2019) and DANN architecture (Ganin and Lempitsky, 2015) to build the model. In the NER task, the data is labeled with Tie, Break, and the task weight is adjusted according to the loss change rate of each task using Dynamic Weight Average (DWA) (Liu et al., 2019). This study used two different source domain data sets. The experimental results show that Tie, Break can improve the results of the model: DWA can have better performance in the results; the combination of parameter generation network and gradient reversal layer can be used for every good learning in different domain.
KW - Aspect-Based Sentiment Analysis
KW - Gradient Adversarial Layer
KW - Named Entity Recognition
KW - Parameter Generation Network
UR - http://www.scopus.com/inward/record.url?scp=85127396919&partnerID=8YFLogxK
M3 - 會議論文篇章
AN - SCOPUS:85127396919
T3 - ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing
SP - 202
EP - 209
BT - ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing
A2 - Lee, Lung-Hao
A2 - Chang, Chia-Hui
A2 - Chen, Kuan-Yu
PB - The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
T2 - 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021
Y2 - 15 October 2021 through 16 October 2021
ER -