@inproceedings{liu-etal-2022-hitmi,
title = "{HITMI}{\&}{T} at {S}em{E}val-2022 Task 4: Investigating Task-Adaptive Pretraining And Attention Mechanism On {PCL} Detection",
author = "Liu, Zihang and
He, Yancheng and
Zhuang, Feiqing and
Xu, Bing",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.59",
doi = "10.18653/v1/2022.semeval-1.59",
pages = "438--444",
abstract = "This paper describes the system for the Semeval-2022 Task4 {''}Patronizing and Condescending Language Detection{''}.An entity engages in Patronizing and Condescending Language(PCL) when its language use shows a superior attitude towards others or depicts them in a compassionate way. The task contains two parts. The first one is to identify whether the sentence is PCL, and the second one is to categorize PCL. Through experimental verification, the Roberta-based model will be used in our system. Respectively, for subtask 1, that is, to judge whether a sentence is PCL, the method of retraining the model with specific task data is adopted, and the method of splicing [CLS] and the keyword representation of the last three layers as the representation of the sentence; for subtask 2, that is, to judge the PCL type of the sentence, in addition to using the same method as task1, the method of selecting a special loss for Multi-label text classification is applied. We give a clear ablation experiment and give the effect of each method on the final result. Our project ranked 11th out of 79 teams participating in subtask 1 and 6th out of 49 teams participating in subtask 2.",
}
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<abstract>This paper describes the system for the Semeval-2022 Task4 ”Patronizing and Condescending Language Detection”.An entity engages in Patronizing and Condescending Language(PCL) when its language use shows a superior attitude towards others or depicts them in a compassionate way. The task contains two parts. The first one is to identify whether the sentence is PCL, and the second one is to categorize PCL. Through experimental verification, the Roberta-based model will be used in our system. Respectively, for subtask 1, that is, to judge whether a sentence is PCL, the method of retraining the model with specific task data is adopted, and the method of splicing [CLS] and the keyword representation of the last three layers as the representation of the sentence; for subtask 2, that is, to judge the PCL type of the sentence, in addition to using the same method as task1, the method of selecting a special loss for Multi-label text classification is applied. We give a clear ablation experiment and give the effect of each method on the final result. Our project ranked 11th out of 79 teams participating in subtask 1 and 6th out of 49 teams participating in subtask 2.</abstract>
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%0 Conference Proceedings
%T HITMI&T at SemEval-2022 Task 4: Investigating Task-Adaptive Pretraining And Attention Mechanism On PCL Detection
%A Liu, Zihang
%A He, Yancheng
%A Zhuang, Feiqing
%A Xu, Bing
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F liu-etal-2022-hitmi
%X This paper describes the system for the Semeval-2022 Task4 ”Patronizing and Condescending Language Detection”.An entity engages in Patronizing and Condescending Language(PCL) when its language use shows a superior attitude towards others or depicts them in a compassionate way. The task contains two parts. The first one is to identify whether the sentence is PCL, and the second one is to categorize PCL. Through experimental verification, the Roberta-based model will be used in our system. Respectively, for subtask 1, that is, to judge whether a sentence is PCL, the method of retraining the model with specific task data is adopted, and the method of splicing [CLS] and the keyword representation of the last three layers as the representation of the sentence; for subtask 2, that is, to judge the PCL type of the sentence, in addition to using the same method as task1, the method of selecting a special loss for Multi-label text classification is applied. We give a clear ablation experiment and give the effect of each method on the final result. Our project ranked 11th out of 79 teams participating in subtask 1 and 6th out of 49 teams participating in subtask 2.
%R 10.18653/v1/2022.semeval-1.59
%U https://aclanthology.org/2022.semeval-1.59
%U https://doi.org/10.18653/v1/2022.semeval-1.59
%P 438-444
Markdown (Informal)
[HITMI&T at SemEval-2022 Task 4: Investigating Task-Adaptive Pretraining And Attention Mechanism On PCL Detection](https://aclanthology.org/2022.semeval-1.59) (Liu et al., SemEval 2022)
ACL