TextLearner at SemEval-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text

Zhishen Yang, Lars Wolfsteller, Naoaki Okazaki


Abstract
This paper describes the emphasis selection system of the team TextLearner for SemEval 2020 Task 10: Emphasis Selection For Written Text in Visual Media. The system aims to learn the emphasis selection distribution using contextual representations extracted from pre-trained language models and a two-staged ranking model. The experimental results demonstrate the strong contextual representation power of the recent advanced transformer-based language model RoBERTa, which can be exploited using a simple but effective architecture on top.
Anthology ID:
2020.semeval-1.221
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:
1691–1697
Language:
URL:
https://aclanthology.org/2020.semeval-1.221
DOI:
10.18653/v1/2020.semeval-1.221
Bibkey:
Cite (ACL):
Zhishen Yang, Lars Wolfsteller, and Naoaki Okazaki. 2020. TextLearner at SemEval-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1691–1697, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
TextLearner at SemEval-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text (Yang et al., SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.221.pdf
Data
MultiNLI