Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation
Fahmida Alam, Md Asiful Islam, Robert Vacareanu, Mihai Surdeanu
Abstract
We introduce a meta dataset for few-shot relation extraction, which includes two datasets derived from existing supervised relation extraction datasets – NYT29 (Takanobu et al., 2019; Nayak and Ng, 2020) and WIKI- DATA (Sorokin and Gurevych, 2017) – as well as a few-shot form of the TACRED dataset (Sabo et al., 2021). Importantly, all these few-shot datasets were generated under realistic assumptions such as: the test relations are different from any relations a model might have seen before, limited training data, and a preponderance of candidate relation mentions that do not correspond to any of the relations of interest. Using this large resource, we conduct a comprehensive evaluation of six recent few-shot relation extraction methods, and observe that no method comes out as a clear winner. Further, the overall performance on this task is low, indicating substantial need for future research. We release all versions of the data, i.e., both supervised and few-shot, for future research.
BibTeX
@inproceedings{alam-etal-2024-towards,
title = "Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation",
author = "Alam, Fahmida and
Islam, Md Asiful and
Vacareanu, Robert and
Surdeanu, Mihai",
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.1442/",
pages = "16592--16606"
}