Meta-based self-training and re-weighting for aspect-based sentiment analysis
Aspect-based sentiment analysis (ABSA) means to identify fine-grained aspects, opinions, and sentiment polarities. Recent ABSA research focuses on utilizing multi-task learning (MTL) to achieve less computational costs and better performance. However, there are certain limits in MTL-based ABSA. For...
| Main Authors: | , , , , |
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| Drugi avtorji: | |
| Format: | Journal Article |
| Jezik: | English |
| Izdano: |
2022
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| Online dostop: | https://hdl.handle.net/10356/163145 |
| _version_ | 1826121746051235840 |
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| author | He, Kai Mao, Rui Gong, Tieliang Li, Chen Cambria, Erik |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering He, Kai Mao, Rui Gong, Tieliang Li, Chen Cambria, Erik |
| author_sort | He, Kai |
| collection | NTU |
| description | Aspect-based sentiment analysis (ABSA) means to identify fine-grained aspects, opinions, and sentiment polarities. Recent ABSA research focuses on utilizing multi-task learning (MTL) to achieve less computational costs and better performance. However, there are certain limits in MTL-based ABSA. For example, unbalanced labels and sub-task learning difficulties may result in the biases that some labels and sub-tasks are overfitting, while the others are underfitting. To address these issues, inspired by neuro-symbolic learning systems, we propose a meta-based self-training method with a meta-weighter (MSM). We believe that a generalizable model can be achieved by appropriate symbolic representation selection (in-domain knowledge) and effective learning control (regulation) in a neural system. Thus, MSM trains a teacher model to generate in-domain knowledge (e.g., unlabeled data selection and pseudo-label generation), where the generated pseudo-labels are used by a student model for supervised learning. Then, the meta-weighter of MSM is jointly trained with the student model to provide each instance with sub-task-specific weights to coordinate their convergence rates, balancing class labels, and alleviating noise impacts introduced from self-training. The following experiments indicate that MSM can utilize 50% labeled data to achieve comparable results to state-of-arts models in ABSA and outperform them with all labeled data. |
| first_indexed | 2024-10-01T05:37:12Z |
| format | Journal Article |
| id | ntu-10356/163145 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:37:12Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1631452022-11-25T02:13:47Z Meta-based self-training and re-weighting for aspect-based sentiment analysis He, Kai Mao, Rui Gong, Tieliang Li, Chen Cambria, Erik School of Computer Science and Engineering Engineering::Computer science and engineering Aspect-Based Sentiment Analysis Meta Learning Aspect-based sentiment analysis (ABSA) means to identify fine-grained aspects, opinions, and sentiment polarities. Recent ABSA research focuses on utilizing multi-task learning (MTL) to achieve less computational costs and better performance. However, there are certain limits in MTL-based ABSA. For example, unbalanced labels and sub-task learning difficulties may result in the biases that some labels and sub-tasks are overfitting, while the others are underfitting. To address these issues, inspired by neuro-symbolic learning systems, we propose a meta-based self-training method with a meta-weighter (MSM). We believe that a generalizable model can be achieved by appropriate symbolic representation selection (in-domain knowledge) and effective learning control (regulation) in a neural system. Thus, MSM trains a teacher model to generate in-domain knowledge (e.g., unlabeled data selection and pseudo-label generation), where the generated pseudo-labels are used by a student model for supervised learning. Then, the meta-weighter of MSM is jointly trained with the student model to provide each instance with sub-task-specific weights to coordinate their convergence rates, balancing class labels, and alleviating noise impacts introduced from self-training. The following experiments indicate that MSM can utilize 50% labeled data to achieve comparable results to state-of-arts models in ABSA and outperform them with all labeled data. This work has been supported by grant Key Research and Development Program of Ningxia Hui Nationality Autonomous Region (2022BEG02025); grant Key Research and Development Program of Shaanxi Province (2021GXLH-Z095); grant RIE2020 Industry Alignment Fund aˆ Industry Collaboration Projects (IAF-ICP) Funding Initiative; grant 61721002 from the Innovative Research Group of the National Natural Science Foundation of China, and grant IRT 17R86 from the Innovation Research Team of the Ministry of Education, Project of China Knowledge Centre for Engineering Science and Technology. 2022-11-25T02:13:47Z 2022-11-25T02:13:47Z 2022 Journal Article He, K., Mao, R., Gong, T., Li, C. & Cambria, E. (2022). Meta-based self-training and re-weighting for aspect-based sentiment analysis. IEEE Transactions On Affective Computing, 3202831-. https://dx.doi.org/10.1109/TAFFC.2022.3202831 1949-3045 https://hdl.handle.net/10356/163145 10.1109/TAFFC.2022.3202831 2-s2.0-85137543873 3202831 en IEEE Transactions on Affective Computing © 2022 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Aspect-Based Sentiment Analysis Meta Learning He, Kai Mao, Rui Gong, Tieliang Li, Chen Cambria, Erik Meta-based self-training and re-weighting for aspect-based sentiment analysis |
| title | Meta-based self-training and re-weighting for aspect-based sentiment analysis |
| title_full | Meta-based self-training and re-weighting for aspect-based sentiment analysis |
| title_fullStr | Meta-based self-training and re-weighting for aspect-based sentiment analysis |
| title_full_unstemmed | Meta-based self-training and re-weighting for aspect-based sentiment analysis |
| title_short | Meta-based self-training and re-weighting for aspect-based sentiment analysis |
| title_sort | meta based self training and re weighting for aspect based sentiment analysis |
| topic | Engineering::Computer science and engineering Aspect-Based Sentiment Analysis Meta Learning |
| url | https://hdl.handle.net/10356/163145 |
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