@inproceedings{wang-etal-2024-use,
    title = "On the Use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction",
    author = "Wang, Jianwei  and
      Wang, Tianyin  and
      Zeng, Ziqian",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1088",
    pages = "12423--12434",
    abstract = "The superior performance of supervised classification methods in the information extraction (IE) area heavily relies on a large amount of gold standard data. Recent zero-shot classification methods converted the task to other NLP tasks (e.g., textual entailment) and used off-the-shelf models of these NLP tasks to directly perform inference on the test data without using a large amount of IE annotation data. A potentially valuable by-product of these methods is the large-scale silver standard data, i.e., pseudo-labeled data by the off-the-shelf models of other NLP tasks. However, there is no further investigation into the use of these data. In this paper, we propose a new framework, Clean-LaVe, which aims to utilize silver standard data to enhance the zero-shot performance. Clean-LaVe includes four phases: (1) Obtaining silver data; (2) Identifying relatively clean data from silver data; (3) Finetuning the off-the-shelf model using clean data; (4) Inference on the test data. The experimental results show that Clean-LaVe can outperform the baseline by 5{\%} and 6{\%} on TACRED and Wiki80 dataset in the zero-shot relation classification task, and by 3{\%} {\textasciitilde}7 {\%} on Smile (Korean and Polish) in the zero-shot cross-lingual relation classification task, and by 8{\%} on ACE05-E+ in the zero-shot event argument classification task.",
}

The superior performance of supervised classification methods in the information extraction (IE) area heavily relies on a large amount of gold standard data. Recent zero-shot classification methods converted the task to other NLP tasks (e.g., textual entailment) and used off-the-shelf models of these NLP tasks to directly perform inference on the test data without using a large amount of IE annotation data. A potentially valuable by-product of these methods is the large-scale silver standard data, i.e., pseudo-labeled data by the off-the-shelf models of other NLP tasks. However, there is no further investigation into the use of these data. In this paper, we propose a new framework, Clean-LaVe, which aims to utilize silver standard data to enhance the zero-shot performance. Clean-LaVe includes four phases: (1) Obtaining silver data; (2) Identifying relatively clean data from silver data; (3) Finetuning the off-the-shelf model using clean data; (4) Inference on the test data. The experimental results show that Clean-LaVe can outperform the baseline by 5{\%} and 6{\%} on TACRED and Wiki80 dataset in the zero-shot relation classification task, and by 3{\%} {\textasciitilde}7 {\%} on Smile (Korean and Polish) in the zero-shot cross-lingual relation classification task, and by 8{\%} on ACE05-E+ in the zero-shot event argument classification task.
Wang, Jianwei Wang, Tianyin Zeng, Ziqian
On the Use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction