Abstract
There is heavy dependence on the large amount of annotated data in most existing methods of relation classification, which is a serious problem. Besides, we cannot learn by leveraging past learned knowledge in most situation, which means it can only train from scratch to learn new tasks. Motivated from humans’ ability of learning effectively from few samples and learning quickly by utilizing learned knowledge, we use both meta network based on co-reference resolution and prototypical network based on co-reference resolution to resolve the problem of few-shot relation classification for crossing-sentence task. Both of the two network aim to learn a transferrable deep distance metric to recognize new relation categories given very few labelled samples. Instead of single sentence, paragraphs containing multi-sentence is a major concern in the experiment. The results demonstrate that our approach performs well and achieves high precision.
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Acknowledgments
This research work is supported by National Natural Science Foundation of China (No. 61402220, No. 61502221), the Philosophy and Social Science Foundation of Hunan Province (No. 16YBA323), Scientific Research Fund of Hunan Provincial Education Department for excellent talents (No.18B279).
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Wen, W., Liu, Y., Ouyang, C. (2020). Few-Shot Learning for Crossing-Sentence Relation Classification. In: Zhai, G., Zhou, J., Yang, H., An, P., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2019. Communications in Computer and Information Science, vol 1181. Springer, Singapore. https://doi.org/10.1007/978-981-15-3341-9_13
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