Abstract
In visual communication, the ability of a short piece of text to catch someone’s eye in a single glance or from a distance is of paramount importance. In our approach to the SemEval-2020 task “Emphasis Selection For Written Text in Visual Media”, we use contextualized word representations from a pretrained model of the state-of-the-art BERT architecture together with a stacked bidirectional GRU network to predict token-level emphasis probabilities. For tackling low inter-annotator agreement in the dataset, we attempt to model multiple annotators jointly by introducing initialization with agreement dependent noise to a crowd layer architecture. We found our approach to both perform substantially better than initialization with identities for this purpose and to outperform a baseline trained with token level majority voting. Our submission system reaches substantially higher Match m on the development set than the task baseline (0.779), but only slightly outperforms the test set baseline (0.754) using a three model ensemble.- Anthology ID:
- 2020.semeval-1.222
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 1698–1703
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.222
- DOI:
- 10.18653/v1/2020.semeval-1.222
- Cite (ACL):
- Kevin Glocker and Stefanos Andreas Markianos Wright. 2020. TëXtmarkers at SemEval-2020 Task 10: Emphasis Selection with Agreement Dependent Crowd Layers. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1698–1703, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- TëXtmarkers at SemEval-2020 Task 10: Emphasis Selection with Agreement Dependent Crowd Layers (Glocker & Markianos Wright, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2020.semeval-1.222.pdf