Abstract
Few-shot relation extraction enables the model to extract new relations and achieve impressive success. However, when new relations come from new domains, semantic and syntactic differences cause a dramatic drop in model performance. Therefore, the domain adaptive few-shot relation extraction task becomes important. However, existing works identify relations more by entities than by context, which makes it difficult to effectively distinguish different relations with similar entity semantic backgrounds in professional domains. In this paper, we propose a method called multi-view context representation with discriminative semantic learning (MCDS). This method learns discriminative entity representations and enhances the use of relational information in context, thus effectively distinguishing different relations with similar entity semantics. Meanwhile, it filters partial entity information from the global information through an information filtering mechanism to obtain more comprehensive global information. We perform extensive experiments on the FewRel 2.0 dataset and the results show an average gain of 2.43% in the accuracy of our model on all strong baselines.
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This work was supported by No. XDC02050200.
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Zhai, M., Dai, F., Gu, X., Fan, H., Liu, D., Li, B. (2024). Learning Discriminative Semantic and Multi-view Context for Domain Adaptive Few-Shot Relation Extraction. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1969. Springer, Singapore. https://doi.org/10.1007/978-981-99-8184-7_22
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