{"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/deep-learning-brasil-at-absapt-2022","title":"Deep Learning Brasil at ABSAPT 2022: Portuguese Transformer Ensemble Approaches","arxiv_id":"2311.05051","date":"2023-11-08","proceeding":null,"authors":["Juliana Resplande Santanna Gomes","Eduardo Augusto Santos Garcia","Adalberto Ferreira Barbosa Junior","Ruan Chaves Rodrigues","Diogo Fernandes Costa Silva","Dyonnatan Ferreira Maia","Nádia Félix Felipe da Silva","Arlindo Rodrigues Galvão Filho","Anderson da Silva Soares"],"abstract":"Aspect-based Sentiment Analysis (ABSA) is a task whose objective is to classify the individual sentiment polarity of all entities, called aspects, in a sentence. The task is composed of two subtasks: Aspect Term Extraction (ATE), identify all aspect terms in a sentence; and Sentiment Orientation Extraction (SOE), given a sentence and its aspect terms, the task is to determine the sentiment polarity of each aspect term (positive, negative or neutral). This article presents we present our participation in Aspect-Based Sentiment Analysis in Portuguese (ABSAPT) 2022 at IberLEF 2022. We submitted the best performing systems, achieving new state-of-the-art results on both subtasks.","url_abs":"https://arxiv.org/abs/2311.05051v1","url_pdf":"https://arxiv.org/pdf/2311.05051v1.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":"deep-learning-brasil-at-absapt-2022","repo_url":"https://github.com/ju-resplande/dlb_absapt2022","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"aspect-category-polarity","task_name":"Aspect Category Polarity"},{"task_slug":"aspect-extraction","task_name":"Aspect Extraction"},{"task_slug":"aspect-term-extraction-and-sentiment","task_name":"Aspect Term Extraction and Sentiment Classification"},{"task_slug":"aspect-based-sentiment-analysis-1","task_name":"Aspect-Based Sentiment Analysis"},{"task_slug":"aspect-based-sentiment-analysis","task_name":"Aspect-Based Sentiment Analysis (ABSA)"},{"task_slug":"aspect-oriented-opinion-extraction","task_name":"Aspect-oriented  Opinion Extraction"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"term-extraction","task_name":"Term Extraction"}],"methods":[{"method_slug":"adafactor","method_name":"Adafactor"},{"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":"bpe","method_name":"BPE"},{"method_slug":"deberta","method_name":"DeBERTa"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"glu","method_name":"Gated Linear Unit"},{"method_slug":"inverse-square-root-schedule","method_name":"Inverse Square Root Schedule"},{"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":"roberta","method_name":"RoBERTa"},{"method_slug":"sentencepiece","method_name":"SentencePiece"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"t5","method_name":"T5"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}