{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ucas-iie-nlp-at-semeval-2023-task-12","title":"UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis","arxiv_id":"2306.01093","date":"2023-06-01","proceeding":null,"authors":["Dou Hu","Lingwei Wei","Yaxin Liu","Wei Zhou","Songlin Hu"],"abstract":"This paper describes our system designed for SemEval-2023 Task 12: Sentiment analysis for African languages. The challenge faced by this task is the scarcity of labeled data and linguistic resources in low-resource settings. To alleviate these, we propose a generalized multilingual system SACL-XLMR for sentiment analysis on low-resource languages. Specifically, we design a lexicon-based multilingual BERT to facilitate language adaptation and sentiment-aware representation learning. Besides, we apply a supervised adversarial contrastive learning technique to learn sentiment-spread structured representations and enhance model generalization. Our system achieved competitive results, largely outperforming baselines on both multilingual and zero-shot sentiment classification subtasks. Notably, the system obtained the 1st rank on the zero-shot classification subtask in the official ranking. Extensive experiments demonstrate the effectiveness of our system.","url_abs":"https://arxiv.org/abs/2306.01093v1","url_pdf":"https://arxiv.org/pdf/2306.01093v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ucas-iie-nlp-at-semeval-2023-task-12","repo_url":"https://github.com/zerohd4869/sacl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"},{"task_slug":"zero-shot-learning","task_name":"Zero-Shot Learning"},{"task_slug":"zero-shot-sentiment-classification","task_name":"Zero-shot Sentiment Classification"},{"task_slug":null,"task_name":"zero-shot-classification"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/zero-shot-sentiment-classification-on","task":"Zero-shot Sentiment Classification","dataset":"AfriSenti","model":"SACL-XLMR","rank_in_archive_order":1,"of":5,"metrics":{"weighted-F1 score":"0.589"},"uses_additional_data":false},{"leaderboard":"/sota/zero-shot-sentiment-classification-on","task":"Zero-shot Sentiment Classification","dataset":"AfriSenti","model":"AfroXLMR","rank_in_archive_order":2,"of":5,"metrics":{"weighted-F1 score":"0.561"},"uses_additional_data":false},{"leaderboard":"/sota/zero-shot-sentiment-classification-on","task":"Zero-shot Sentiment Classification","dataset":"AfriSenti","model":"AfriBERTa","rank_in_archive_order":3,"of":5,"metrics":{"weighted-F1 score":"0.439"},"uses_additional_data":false},{"leaderboard":"/sota/zero-shot-sentiment-classification-on","task":"Zero-shot Sentiment Classification","dataset":"AfriSenti","model":"XLM-R","rank_in_archive_order":4,"of":5,"metrics":{"weighted-F1 score":"0.399"},"uses_additional_data":false},{"leaderboard":"/sota/zero-shot-sentiment-classification-on","task":"Zero-shot Sentiment Classification","dataset":"AfriSenti","model":"Random","rank_in_archive_order":5,"of":5,"metrics":{"weighted-F1 score":"0.34"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}