Multi-task Domain Adaptation for Sequence Tagging

@inproceedings{Peng2016MultitaskDA,
  title={Multi-task Domain Adaptation for Sequence Tagging},
  author={Nanyun Peng and Mark Dredze},
  booktitle={Rep4NLP@ACL},
  year={2016}
}
  • Nanyun Peng, Mark Dredze
  • Published in Rep4NLP@ACL 2016
  • Computer Science
  • Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains. Traditional domain adaptation only considers adapting for one task. In this paper, we explore multi-task representation learning under the domain adaptation scenario. We propose a neural network framework that supports domain adaptation for multiple tasks simultaneously, and learns shared representations that better generalize for domain… CONTINUE READING

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

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 36 CITATIONS

    Neural Chinese Word Segmentation with Dictionary Knowledge

    VIEW 4 EXCERPTS
    CITES METHODS
    HIGHLY INFLUENCED

    Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation

    VIEW 7 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    DEXTER - Data EXTraction & Entity Recognition for Low Resource Datasets

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 47 REFERENCES

    Deep Multi-Task Learning with Shared Memory

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Multitask Learning

    VIEW 11 EXCERPTS
    HIGHLY INFLUENTIAL