@inproceedings{alam-etal-2023-gmnlp,
title = "{GMNLP} at {S}em{E}val-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters",
author = "Alam, Md Mahfuz Ibn and
Xie, Ruoyu and
Faisal, Fahim and
Anastasopoulos, Antonios",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.163",
doi = "10.18653/v1/2023.semeval-1.163",
pages = "1172--1182",
abstract = "This report describes GMU{'}s sentiment analysis system for the SemEval-2023 shared task AfriSenti-SemEval. We participated in all three sub-tasks: Monolingual, Multilingual, and Zero-Shot. Our approach uses models initialized with AfroXLMR-large, a pre-trained multilingual language model trained on African languages and fine-tuned correspondingly. We also introduce augmented training data along with original training data. Alongside finetuning, we perform phylogeny-based adapter-tuning to create several models and ensemble the best models for the final submission. Our system achieves the best F1-score on track 5: Amharic, with 6.2 points higher F1-score than the second-best performing system on this track. Overall, our system ranks 5th among the 10 systems participating in all 15 tracks.",
}
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<abstract>This report describes GMU’s sentiment analysis system for the SemEval-2023 shared task AfriSenti-SemEval. We participated in all three sub-tasks: Monolingual, Multilingual, and Zero-Shot. Our approach uses models initialized with AfroXLMR-large, a pre-trained multilingual language model trained on African languages and fine-tuned correspondingly. We also introduce augmented training data along with original training data. Alongside finetuning, we perform phylogeny-based adapter-tuning to create several models and ensemble the best models for the final submission. Our system achieves the best F1-score on track 5: Amharic, with 6.2 points higher F1-score than the second-best performing system on this track. Overall, our system ranks 5th among the 10 systems participating in all 15 tracks.</abstract>
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%0 Conference Proceedings
%T GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters
%A Alam, Md Mahfuz Ibn
%A Xie, Ruoyu
%A Faisal, Fahim
%A Anastasopoulos, Antonios
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F alam-etal-2023-gmnlp
%X This report describes GMU’s sentiment analysis system for the SemEval-2023 shared task AfriSenti-SemEval. We participated in all three sub-tasks: Monolingual, Multilingual, and Zero-Shot. Our approach uses models initialized with AfroXLMR-large, a pre-trained multilingual language model trained on African languages and fine-tuned correspondingly. We also introduce augmented training data along with original training data. Alongside finetuning, we perform phylogeny-based adapter-tuning to create several models and ensemble the best models for the final submission. Our system achieves the best F1-score on track 5: Amharic, with 6.2 points higher F1-score than the second-best performing system on this track. Overall, our system ranks 5th among the 10 systems participating in all 15 tracks.
%R 10.18653/v1/2023.semeval-1.163
%U https://aclanthology.org/2023.semeval-1.163
%U https://doi.org/10.18653/v1/2023.semeval-1.163
%P 1172-1182
Markdown (Informal)
[GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters](https://aclanthology.org/2023.semeval-1.163) (Alam et al., SemEval 2023)
ACL