Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification

@inproceedings{Ye2019MultiLevelMA,
  title={Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification},
  author={Zhi-Xiu Ye and Zhen-Hua Ling},
  booktitle={ACL},
  year={2019}
}
  • Zhi-Xiu Ye, Zhen-Hua Ling
  • Published in ACL 2019
  • Computer Science
  • This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. Previous studies on this topic adopt prototypical networks, which calculate the embedding vector of a query instance and the prototype vector of each support set independently. In contrast, our proposed MLMAN model encodes the query instance and each support set in an interactive way by considering their matching information at both local and instance levels. The final class… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    Figures, Tables, and Topics from this paper.

    Citations

    Publications citing this paper.

    OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction

    VIEW 2 EXCERPTS
    CITES BACKGROUND & METHODS

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 33 REFERENCES

    Prototypical Networks for Few-shot Learning

    VIEW 9 EXCERPTS
    HIGHLY INFLUENTIAL

    Enhanced LSTM for Natural Language Inference

    VIEW 4 EXCERPTS

    Relation Classification via Convolutional Deep Neural Network

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    Meta Networks

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL