@inproceedings{vania-lee-and-andrea-pierleoni-2022-improving,
    title = "Improving Distantly Supervised Document-Level Relation Extraction Through Natural Language Inference",
    author = "Vania, Clara  and
      Lee, Grace  and
      Pierleoni, Andrea",
    editor = "Cherry, Colin  and
      Fan, Angela  and
      Foster, George  and
      Haffari, Gholamreza (Reza)  and
      Khadivi, Shahram  and
      Peng, Nanyun (Violet)  and
      Ren, Xiang  and
      Shareghi, Ehsan  and
      Swayamdipta, Swabha",
    booktitle = "Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing",
    month = jul,
    year = "2022",
    address = "Hybrid",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.deeplo-1.2",
    doi = "10.18653/v1/2022.deeplo-1.2",
    pages = "14--20",
    abstract = "The distant supervision (DS) paradigm has been widely used for relation extraction (RE) to alleviate the need for expensive annotations. However, it suffers from noisy labels, which leads to worse performance than models trained on human-annotated data, even when trained using hundreds of times more data. We present a systematic study on the use of natural language inference (NLI) to improve distantly supervised document-level RE. We apply NLI in three scenarios: (i) as a filter for denoising DS labels, (ii) as a filter for model prediction, and (iii) as a standalone RE model. Our results show that NLI filtering consistently improves performance, reducing the performance gap with a model trained on human-annotated data by 2.3 F1.",
}