@inproceedings{luo-etal-2019-semi,
    title = "Semi-Supervised Teacher-Student Architecture for Relation Extraction",
    author = "Luo, Fan  and
      Nagesh, Ajay  and
      Sharp, Rebecca  and
      Surdeanu, Mihai",
    editor = "Martins, Andre  and
      Vlachos, Andreas  and
      Kozareva, Zornitsa  and
      Ravi, Sujith  and
      Lampouras, Gerasimos  and
      Niculae, Vlad  and
      Kreutzer, Julia",
    booktitle = "Proceedings of the Third Workshop on Structured Prediction for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-1505/",
    doi = "10.18653/v1/W19-1505",
    pages = "29--37",
    abstract = "Generating a large amount of training data for information extraction (IE) is either costly (if annotations are created manually), or runs the risk of introducing noisy instances (if distant supervision is used). On the other hand, semi-supervised learning (SSL) is a cost-efficient solution to combat lack of training data. In this paper, we adapt Mean Teacher (Tarvainen and Valpola, 2017), a denoising SSL framework to extract semantic relations between pairs of entities. We explore the sweet spot of amount of supervision required for good performance on this binary relation extraction task. Additionally, different syntax representations are incorporated into our models to enhance the learned representation of sentences. We evaluate our approach on the Google-IISc Distant Supervision (GDS) dataset, which removes test data noise present in all previous distance supervision datasets, which makes it a reliable evaluation benchmark (Jat et al., 2017). Our results show that the SSL Mean Teacher approach nears the performance of fully-supervised approaches even with only 10{\%} of the labeled corpus. Further, the syntax-aware model outperforms other syntax-free approaches across all levels of supervision."
}