Abstract
We describe a novel method for efficiently eliciting scalar annotations for dataset construction and system quality estimation by human judgments. We contrast direct assessment (annotators assign scores to items directly), online pairwise ranking aggregation (scores derive from annotator comparison of items), and a hybrid approach (EASL: Efficient Annotation of Scalar Labels) proposed here. Our proposal leads to increased correlation with ground truth, at far greater annotator efficiency, suggesting this strategy as an improved mechanism for dataset creation and manual system evaluation.- Anthology ID:
- P18-1020
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 208–218
- Language:
- URL:
- https://aclanthology.org/P18-1020
- DOI:
- 10.18653/v1/P18-1020
- Cite (ACL):
- Keisuke Sakaguchi and Benjamin Van Durme. 2018. Efficient Online Scalar Annotation with Bounded Support. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 208–218, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Efficient Online Scalar Annotation with Bounded Support (Sakaguchi & Van Durme, ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/P18-1020.pdf
- Data
- WMT 2016