@inproceedings{morio-etal-2020-hitachi,
title = "Hitachi at {S}em{E}val-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models",
author = "Morio, Gaku and
Morishita, Terufumi and
Ozaki, Hiroaki and
Miyoshi, Toshinori",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.216",
doi = "10.18653/v1/2020.semeval-1.216",
pages = "1658--1664",
abstract = "This paper shows our system for SemEval-2020 task 10, Emphasis Selection for Written Text in Visual Media. Our strategy is two-fold. First, we propose fine-tuning many pre-trained language models, predicting an emphasis probability distribution over tokens. Then, we propose stacking a trainable distribution fusion DistFuse system to fuse the predictions of the fine-tuned models. Experimental results show tha DistFuse is comparable or better when compared with a naive average ensemble. As a result, we were ranked 2nd amongst 31 teams.",
}
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<abstract>This paper shows our system for SemEval-2020 task 10, Emphasis Selection for Written Text in Visual Media. Our strategy is two-fold. First, we propose fine-tuning many pre-trained language models, predicting an emphasis probability distribution over tokens. Then, we propose stacking a trainable distribution fusion DistFuse system to fuse the predictions of the fine-tuned models. Experimental results show tha DistFuse is comparable or better when compared with a naive average ensemble. As a result, we were ranked 2nd amongst 31 teams.</abstract>
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%0 Conference Proceedings
%T Hitachi at SemEval-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models
%A Morio, Gaku
%A Morishita, Terufumi
%A Ozaki, Hiroaki
%A Miyoshi, Toshinori
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F morio-etal-2020-hitachi
%X This paper shows our system for SemEval-2020 task 10, Emphasis Selection for Written Text in Visual Media. Our strategy is two-fold. First, we propose fine-tuning many pre-trained language models, predicting an emphasis probability distribution over tokens. Then, we propose stacking a trainable distribution fusion DistFuse system to fuse the predictions of the fine-tuned models. Experimental results show tha DistFuse is comparable or better when compared with a naive average ensemble. As a result, we were ranked 2nd amongst 31 teams.
%R 10.18653/v1/2020.semeval-1.216
%U https://aclanthology.org/2020.semeval-1.216
%U https://doi.org/10.18653/v1/2020.semeval-1.216
%P 1658-1664
Markdown (Informal)
[Hitachi at SemEval-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models](https://aclanthology.org/2020.semeval-1.216) (Morio et al., SemEval 2020)
ACL