DEANE: Context-Aware Dual-Craft Graph Contrastive Learning for Enhanced Extractive Question Answering
Abstract Extractive Question Answering (EQA) involves extracting accurate answer spans from a background passage in response to a given question. In recent years, there has been significant interest in leveraging Pre-trained Language Models (PLMs) and Graph Convolutional Networks (GCNs) to address E...
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Springer
2025-04-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00801-y |
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| author | Dongfen Ye Jianqiang Zhou Gang Huang |
| author_facet | Dongfen Ye Jianqiang Zhou Gang Huang |
| author_sort | Dongfen Ye |
| collection | DOAJ |
| description | Abstract Extractive Question Answering (EQA) involves extracting accurate answer spans from a background passage in response to a given question. In recent years, there has been significant interest in leveraging Pre-trained Language Models (PLMs) and Graph Convolutional Networks (GCNs) to address EQA tasks. PLMs usually function as context encoders, while GCNs are employed to capture latent semantic relationships between answer spans and the passage/question. This combined approach has shown promise, yielding notable outcomes in EQA performance. However, current graph-based methods encounter a challenge where the graph structure is predefined without sufficient justification. This graph ambiguity can potentially lead to error propagation within the subsequent graph encoder. To alleviate this issue, this paper introduces Dual-craft basEd grAph coNtrastive lEarning (DEANE) for EQA, where the graph structure and node features are context-aware and data-driven. Initially, the passage and question are represented as a connected graph. Subsequently, the adaptive augmentation strategy is introduced to generate two distinct views of the original graph via reparameterization networks, where important graph edges and node features are prioritized. Finally, a multi-view contrastive loss is leveraged to learn latent representations from augmented graphs. Empirically, our method outperforms existing graph-based approaches on six well-established EQA benchmarks. Ablation studies further demonstrate the effectiveness of the proposed approach in mitigating structural ambiguity, enhancing encoder flexibility, and improving model performance through multi-view data integration. |
| format | Article |
| id | doaj-art-0732c5805bdf452884dd10340b2beedc |
| institution | OA Journals |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-0732c5805bdf452884dd10340b2beedc2025-08-20T02:28:08ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-04-0118112310.1007/s44196-025-00801-yDEANE: Context-Aware Dual-Craft Graph Contrastive Learning for Enhanced Extractive Question AnsweringDongfen Ye0Jianqiang Zhou1Gang Huang2College of Electrical and Information Engineering, Quzhou UniversityCollege of Electrical and Information Engineering, Quzhou UniversityCollege of Electrical and Information Engineering, Quzhou UniversityAbstract Extractive Question Answering (EQA) involves extracting accurate answer spans from a background passage in response to a given question. In recent years, there has been significant interest in leveraging Pre-trained Language Models (PLMs) and Graph Convolutional Networks (GCNs) to address EQA tasks. PLMs usually function as context encoders, while GCNs are employed to capture latent semantic relationships between answer spans and the passage/question. This combined approach has shown promise, yielding notable outcomes in EQA performance. However, current graph-based methods encounter a challenge where the graph structure is predefined without sufficient justification. This graph ambiguity can potentially lead to error propagation within the subsequent graph encoder. To alleviate this issue, this paper introduces Dual-craft basEd grAph coNtrastive lEarning (DEANE) for EQA, where the graph structure and node features are context-aware and data-driven. Initially, the passage and question are represented as a connected graph. Subsequently, the adaptive augmentation strategy is introduced to generate two distinct views of the original graph via reparameterization networks, where important graph edges and node features are prioritized. Finally, a multi-view contrastive loss is leveraged to learn latent representations from augmented graphs. Empirically, our method outperforms existing graph-based approaches on six well-established EQA benchmarks. Ablation studies further demonstrate the effectiveness of the proposed approach in mitigating structural ambiguity, enhancing encoder flexibility, and improving model performance through multi-view data integration.https://doi.org/10.1007/s44196-025-00801-yExtractive question answeringAdaptive augmentationMulti-view contrastive learningPre-trained language modelGraph convolutional network |
| spellingShingle | Dongfen Ye Jianqiang Zhou Gang Huang DEANE: Context-Aware Dual-Craft Graph Contrastive Learning for Enhanced Extractive Question Answering International Journal of Computational Intelligence Systems Extractive question answering Adaptive augmentation Multi-view contrastive learning Pre-trained language model Graph convolutional network |
| title | DEANE: Context-Aware Dual-Craft Graph Contrastive Learning for Enhanced Extractive Question Answering |
| title_full | DEANE: Context-Aware Dual-Craft Graph Contrastive Learning for Enhanced Extractive Question Answering |
| title_fullStr | DEANE: Context-Aware Dual-Craft Graph Contrastive Learning for Enhanced Extractive Question Answering |
| title_full_unstemmed | DEANE: Context-Aware Dual-Craft Graph Contrastive Learning for Enhanced Extractive Question Answering |
| title_short | DEANE: Context-Aware Dual-Craft Graph Contrastive Learning for Enhanced Extractive Question Answering |
| title_sort | deane context aware dual craft graph contrastive learning for enhanced extractive question answering |
| topic | Extractive question answering Adaptive augmentation Multi-view contrastive learning Pre-trained language model Graph convolutional network |
| url | https://doi.org/10.1007/s44196-025-00801-y |
| work_keys_str_mv | AT dongfenye deanecontextawaredualcraftgraphcontrastivelearningforenhancedextractivequestionanswering AT jianqiangzhou deanecontextawaredualcraftgraphcontrastivelearningforenhancedextractivequestionanswering AT ganghuang deanecontextawaredualcraftgraphcontrastivelearningforenhancedextractivequestionanswering |