Deep Learning Brasil at ABSAPT 2022: Portuguese Transformer Ensemble Approaches
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 8 Nov 2023.
Builds on
6 of 16 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2020PTT5: Pretraining and validating the T5 model on Brazilian Portuguese datacited by 35
- 2018Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networkscited by 19
- 2021Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment Analysiscited by 10
- 2021Exploring Conditional Text Generation for Aspect-Based Sentiment Analysiscited by 5
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Citing papers per year: 2024: 1.
1 paper.