Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction!
Bruno Taillé, Vincent Guigue, Geoffrey Scoutheeten, Patrick Gallinari
Abstract
Despite efforts to distinguish three different evaluation setups (Bekoulis et al., 2018), numerous end-to-end Relation Extraction (RE) articles present unreliable performance comparison to previous work. In this paper, we first identify several patterns of invalid comparisons in published papers and describe them to avoid their propagation. We then propose a small empirical study to quantify the most common mistake’s impact and evaluate it leads to overestimating the final RE performance by around 5% on ACE05. We also seize this opportunity to study the unexplored ablations of two recent developments: the use of language model pretraining (specifically BERT) and span-level NER. This meta-analysis emphasizes the need for rigor in the report of both the evaluation setting and the dataset statistics. We finally call for unifying the evaluation setting in end-to-end RE.- Anthology ID:
- 2020.emnlp-main.301
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3689–3701
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.301
- DOI:
- 10.18653/v1/2020.emnlp-main.301
- Cite (ACL):
- Bruno Taillé, Vincent Guigue, Geoffrey Scoutheeten, and Patrick Gallinari. 2020. Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction!. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3689–3701, Online. Association for Computational Linguistics.
- Cite (Informal):
- Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction! (Taillé et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.301.pdf
- Code
- btaille/sincere + additional community code
- Data
- SciERC