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.
Anthology ID:
2024.lrec-main.1442
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
16592–16606
Language:
URL:
https://aclanthology.org/2024.lrec-main.1442
DOI:
Bibkey:
Cite (ACL):
Fahmida Alam, Md Asiful Islam, Robert Vacareanu, and Mihai Surdeanu. 2024. Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 16592–16606, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation (Alam et al., LREC-COLING 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.1442.pdf
PDF Search