@inproceedings{christodoulou-2023-nlp-christine,
title = "{NLP}{\_}{CHRISTINE}@{LT}-{EDI}-2023: {R}o{BERT}a {\&} {D}e{BERT}a Fine-tuning for Detecting Signs of Depression from Social Media Text",
author = "Christodoulou, Christina",
editor = "Chakravarthi, Bharathi R. and
Bharathi, B. and
Griffith, Joephine and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ltedi-1.16",
pages = "109--116",
abstract = "The paper describes the system for the 4th Shared task on {``}Detecting Signs of Depression from Social Media Text{''} at LT-EDI@RANLP 2023, which aimed to identify signs of depression on English social media texts. The solution comprised data cleaning and pre-processing, the use of additional data, a method to deal with data imbalance as well as fine-tuning of two transformer-based pre-trained language models, RoBERTa-Large and DeBERTa-V3-Large. Four model architectures were developed by leveraging different word embedding pooling methods, namely a RoBERTa-Large bidirectional GRU model using GRU pooling and three DeBERTa models using CLS pooling, mean pooling and max pooling, respectively. Although ensemble learning of DeBERTa{'}s pooling methods through majority voting was employed for better performance, the RoBERTa bidirectional GRU model managed to receive the 8th place out of 31 submissions with 0.42 Macro-F1 score.",
}
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<abstract>The paper describes the system for the 4th Shared task on “Detecting Signs of Depression from Social Media Text” at LT-EDI@RANLP 2023, which aimed to identify signs of depression on English social media texts. The solution comprised data cleaning and pre-processing, the use of additional data, a method to deal with data imbalance as well as fine-tuning of two transformer-based pre-trained language models, RoBERTa-Large and DeBERTa-V3-Large. Four model architectures were developed by leveraging different word embedding pooling methods, namely a RoBERTa-Large bidirectional GRU model using GRU pooling and three DeBERTa models using CLS pooling, mean pooling and max pooling, respectively. Although ensemble learning of DeBERTa’s pooling methods through majority voting was employed for better performance, the RoBERTa bidirectional GRU model managed to receive the 8th place out of 31 submissions with 0.42 Macro-F1 score.</abstract>
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%0 Conference Proceedings
%T NLP_CHRISTINE@LT-EDI-2023: RoBERTa & DeBERTa Fine-tuning for Detecting Signs of Depression from Social Media Text
%A Christodoulou, Christina
%Y Chakravarthi, Bharathi R.
%Y Bharathi, B.
%Y Griffith, Joephine
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F christodoulou-2023-nlp-christine
%X The paper describes the system for the 4th Shared task on “Detecting Signs of Depression from Social Media Text” at LT-EDI@RANLP 2023, which aimed to identify signs of depression on English social media texts. The solution comprised data cleaning and pre-processing, the use of additional data, a method to deal with data imbalance as well as fine-tuning of two transformer-based pre-trained language models, RoBERTa-Large and DeBERTa-V3-Large. Four model architectures were developed by leveraging different word embedding pooling methods, namely a RoBERTa-Large bidirectional GRU model using GRU pooling and three DeBERTa models using CLS pooling, mean pooling and max pooling, respectively. Although ensemble learning of DeBERTa’s pooling methods through majority voting was employed for better performance, the RoBERTa bidirectional GRU model managed to receive the 8th place out of 31 submissions with 0.42 Macro-F1 score.
%U https://aclanthology.org/2023.ltedi-1.16
%P 109-116
Markdown (Informal)
[NLP_CHRISTINE@LT-EDI-2023: RoBERTa & DeBERTa Fine-tuning for Detecting Signs of Depression from Social Media Text](https://aclanthology.org/2023.ltedi-1.16) (Christodoulou, LTEDI-WS 2023)
ACL