Semi-supervised teacher-student architecture for relation extraction

Fan Luo, Ajay Nagesh, Rebecca Sharp, Mihai Surdeanu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationNLP@NAACL-HLT 2019 - 3rd Workshop on Structured Prediction for NLP, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Number of pages9
ISBN (Electronic)9781950737109
StatePublished - 2021
Event3rd Workshop on Structured Prediction for NLP, NLP@NAACL-HLT 2019 - Minneapolis, United States
Duration: Jun 7 2019 → …

Publication series

NameNLP@NAACL-HLT 2019 - 3rd Workshop on Structured Prediction for NLP, Proceedings


Conference3rd Workshop on Structured Prediction for NLP, NLP@NAACL-HLT 2019
Country/TerritoryUnited States
Period6/7/19 → …

ASJC Scopus subject areas

  • Software
  • Computational Theory and Mathematics


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