@inproceedings{hou-etal-2021-fpai,
title = "{FPAI} at {S}em{E}val-2021 Task 6: {BERT}-{MRC} for Propaganda Techniques Detection",
author = "Hou, Xiaolong and
Ren, Junsong and
Rao, Gang and
Lian, Lianxin and
Ruan, Zhihao and
Mo, Yang and
Shen, JIanping",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.146",
doi = "10.18653/v1/2021.semeval-1.146",
pages = "1056--1060",
abstract = "The objective of subtask 2 of SemEval-2021 Task 6 is to identify techniques used together with the span(s) of text covered by each technique. This paper describes the system and model we developed for the task. We first propose a pipeline system to identify spans, then to classify the technique in the input sequence. But it severely suffers from handling the overlapping in nested span. Then we propose to formulize the task as a question answering task by MRC framework which achieves a better result compared to the pipeline method. Moreover, data augmentation and loss design techniques are also explored to alleviate the problem of data sparse and imbalance. Finally, we attain the 3rd place in the final evaluation phase.",
}
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<abstract>The objective of subtask 2 of SemEval-2021 Task 6 is to identify techniques used together with the span(s) of text covered by each technique. This paper describes the system and model we developed for the task. We first propose a pipeline system to identify spans, then to classify the technique in the input sequence. But it severely suffers from handling the overlapping in nested span. Then we propose to formulize the task as a question answering task by MRC framework which achieves a better result compared to the pipeline method. Moreover, data augmentation and loss design techniques are also explored to alleviate the problem of data sparse and imbalance. Finally, we attain the 3rd place in the final evaluation phase.</abstract>
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%0 Conference Proceedings
%T FPAI at SemEval-2021 Task 6: BERT-MRC for Propaganda Techniques Detection
%A Hou, Xiaolong
%A Ren, Junsong
%A Rao, Gang
%A Lian, Lianxin
%A Ruan, Zhihao
%A Mo, Yang
%A Shen, JIanping
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F hou-etal-2021-fpai
%X The objective of subtask 2 of SemEval-2021 Task 6 is to identify techniques used together with the span(s) of text covered by each technique. This paper describes the system and model we developed for the task. We first propose a pipeline system to identify spans, then to classify the technique in the input sequence. But it severely suffers from handling the overlapping in nested span. Then we propose to formulize the task as a question answering task by MRC framework which achieves a better result compared to the pipeline method. Moreover, data augmentation and loss design techniques are also explored to alleviate the problem of data sparse and imbalance. Finally, we attain the 3rd place in the final evaluation phase.
%R 10.18653/v1/2021.semeval-1.146
%U https://aclanthology.org/2021.semeval-1.146
%U https://doi.org/10.18653/v1/2021.semeval-1.146
%P 1056-1060
Markdown (Informal)
[FPAI at SemEval-2021 Task 6: BERT-MRC for Propaganda Techniques Detection](https://aclanthology.org/2021.semeval-1.146) (Hou et al., SemEval 2021)
ACL