@inproceedings{guo-etal-2020-fpai,
title = "{FPAI} at {S}em{E}val-2020 Task 10: A Query Enhanced Model with {R}o{BERT}a for Emphasis Selection",
author = "Guo, Chenyang and
Hou, Xiaolong and
Ren, Junsong and
Jiang, Lianxin and
Mo, Yang and
Yang, Haiqin and
Shen, Jianping",
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-1.215",
doi = "10.18653/v1/2020.semeval-1.215",
pages = "1652--1657",
abstract = "This paper describes the model we apply in the SemEval-2020 Task 10. We formalize the task of emphasis selection as a simplified query-based machine reading comprehension (MRC) task, i.e. answering a fixed question of {``}Find candidates for emphasis{''}. We propose our subword puzzle encoding mechanism and subword fusion layer to align and fuse subwords. By introducing the semantic prior knowledge of the informative query and some other techniques, we attain the 7th place during the evaluation phase and the first place during train phase.",
}
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<abstract>This paper describes the model we apply in the SemEval-2020 Task 10. We formalize the task of emphasis selection as a simplified query-based machine reading comprehension (MRC) task, i.e. answering a fixed question of “Find candidates for emphasis”. We propose our subword puzzle encoding mechanism and subword fusion layer to align and fuse subwords. By introducing the semantic prior knowledge of the informative query and some other techniques, we attain the 7th place during the evaluation phase and the first place during train phase.</abstract>
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%0 Conference Proceedings
%T FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection
%A Guo, Chenyang
%A Hou, Xiaolong
%A Ren, Junsong
%A Jiang, Lianxin
%A Mo, Yang
%A Yang, Haiqin
%A Shen, Jianping
%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 guo-etal-2020-fpai
%X This paper describes the model we apply in the SemEval-2020 Task 10. We formalize the task of emphasis selection as a simplified query-based machine reading comprehension (MRC) task, i.e. answering a fixed question of “Find candidates for emphasis”. We propose our subword puzzle encoding mechanism and subword fusion layer to align and fuse subwords. By introducing the semantic prior knowledge of the informative query and some other techniques, we attain the 7th place during the evaluation phase and the first place during train phase.
%R 10.18653/v1/2020.semeval-1.215
%U https://aclanthology.org/2020.semeval-1.215
%U https://doi.org/10.18653/v1/2020.semeval-1.215
%P 1652-1657
Markdown (Informal)
[FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection](https://aclanthology.org/2020.semeval-1.215) (Guo et al., SemEval 2020)
ACL