Abstract
General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks. In this work, we present a methodology for collecting high quality training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. Our approach exploits the predictable distributional structure of date-marked news articles to build a denoised corpus – the extraction process filters out low quality examples. We show that a smaller multilingual encoder trained on this corpus performs comparably to the current state-of-the-art (when both receive little to no fine-tuning) on few-shot and standard relation benchmarks in English and Spanish despite using many fewer examples (50k vs. 300mil+).- Anthology ID:
- 2020.coling-main.131
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1505–1512
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.131
- DOI:
- 10.18653/v1/2020.coling-main.131
- Cite (ACL):
- Amith Ananthram, Emily Allaway, and Kathleen McKeown. 2020. Event-Guided Denoising for Multilingual Relation Learning. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1505–1512, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Event-Guided Denoising for Multilingual Relation Learning (Ananthram et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2020.coling-main.131.pdf
- Data
- FewRel, RCV1, SemEval-2010 Task-8