Collaborative Knowledge Infusion for Low-Resource Stance Detection
Stance detection is the view towards a specific target by a given context (e.g. tweets, commercial reviews). Target-related knowledge is often needed to assist stance detection models in understanding the target well and making detection correctly. However, prevailing works for knowledge-infused sta...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020021 |
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| author | Ming Yan Tianyi Zhou Joey W. Tsang Ivor |
| author_facet | Ming Yan Tianyi Zhou Joey W. Tsang Ivor |
| author_sort | Ming Yan |
| collection | DOAJ |
| description | Stance detection is the view towards a specific target by a given context (e.g. tweets, commercial reviews). Target-related knowledge is often needed to assist stance detection models in understanding the target well and making detection correctly. However, prevailing works for knowledge-infused stance detection predominantly incorporate target knowledge from a singular source that lacks knowledge verification in limited domain knowledge. The low-resource training data further increase the challenge for the data-driven large models in this task. To address those challenges, we propose a collaborative knowledge infusion approach for low-resource stance detection tasks, employing a combination of aligned knowledge enhancement and efficient parameter learning techniques. Specifically, our stance detection approach leverages target background knowledge collaboratively from different knowledge sources with the help of knowledge alignment. Additionally, we also introduce the parameter-efficient collaborative adaptor with a staged optimization algorithm, which collaboratively addresses the challenges associated with low-resource stance detection tasks from both network structure and learning perspectives. To assess the effectiveness of our method, we conduct extensive experiments on three public stance detection datasets, including low-resource and cross-target settings. The results demonstrate significant performance improvements compared to the existing stance detection approaches. |
| format | Article |
| id | doaj-art-8e8b0bc2653e429bb0b813a50d291601 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-8e8b0bc2653e429bb0b813a50d2916012025-02-03T10:19:58ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-09-017368269810.26599/BDMA.2024.9020021Collaborative Knowledge Infusion for Low-Resource Stance DetectionMing Yan0Tianyi Zhou Joey1W. Tsang Ivor2Centre for Frontier AI Research, and Institute of High-Performance Computing, Agency for Science Technology and Research, Singapore 138632, SingaporeCentre for Frontier AI Research, and Institute of High-Performance Computing, Agency for Science Technology and Research, Singapore 138632, SingaporeCentre for Frontier AI Research, and Institute of High-Performance Computing, Agency for Science Technology and Research, Singapore 138632, SingaporeStance detection is the view towards a specific target by a given context (e.g. tweets, commercial reviews). Target-related knowledge is often needed to assist stance detection models in understanding the target well and making detection correctly. However, prevailing works for knowledge-infused stance detection predominantly incorporate target knowledge from a singular source that lacks knowledge verification in limited domain knowledge. The low-resource training data further increase the challenge for the data-driven large models in this task. To address those challenges, we propose a collaborative knowledge infusion approach for low-resource stance detection tasks, employing a combination of aligned knowledge enhancement and efficient parameter learning techniques. Specifically, our stance detection approach leverages target background knowledge collaboratively from different knowledge sources with the help of knowledge alignment. Additionally, we also introduce the parameter-efficient collaborative adaptor with a staged optimization algorithm, which collaboratively addresses the challenges associated with low-resource stance detection tasks from both network structure and learning perspectives. To assess the effectiveness of our method, we conduct extensive experiments on three public stance detection datasets, including low-resource and cross-target settings. The results demonstrate significant performance improvements compared to the existing stance detection approaches.https://www.sciopen.com/article/10.26599/BDMA.2024.9020021parameter-efficient learninglow-resource stance detectionknowledge infusion |
| spellingShingle | Ming Yan Tianyi Zhou Joey W. Tsang Ivor Collaborative Knowledge Infusion for Low-Resource Stance Detection Big Data Mining and Analytics parameter-efficient learning low-resource stance detection knowledge infusion |
| title | Collaborative Knowledge Infusion for Low-Resource Stance Detection |
| title_full | Collaborative Knowledge Infusion for Low-Resource Stance Detection |
| title_fullStr | Collaborative Knowledge Infusion for Low-Resource Stance Detection |
| title_full_unstemmed | Collaborative Knowledge Infusion for Low-Resource Stance Detection |
| title_short | Collaborative Knowledge Infusion for Low-Resource Stance Detection |
| title_sort | collaborative knowledge infusion for low resource stance detection |
| topic | parameter-efficient learning low-resource stance detection knowledge infusion |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020021 |
| work_keys_str_mv | AT mingyan collaborativeknowledgeinfusionforlowresourcestancedetection AT tianyizhoujoey collaborativeknowledgeinfusionforlowresourcestancedetection AT wtsangivor collaborativeknowledgeinfusionforlowresourcestancedetection |