Portuguese Aspect Sentiment Triplet Extraction Datasets

Aspect Sentiment Triplet Extraction (ASTE) is an Aspect-Based Sentiment Analysis subtask (ABSA). It aims to extract aspect-opinion pairs from a sentence and identify the sentiment polarity associated with them. For instance, given the sentence ``Large rooms and great breakfast", ASTE outputs the triplet T = {(rooms, large, positive), (breakfast, great, positive)}. Although several approaches to ASBA have recently been proposed, those for Portuguese have been mostly limited to extracting only aspects without addressing ASTE tasks. This work aims to develop a framework based on Deep Learning to perform the Aspect Sentiment Triplet Extraction task in Portuguese. The framework uses BERT as a context-awareness sentence encoder, multiple parallel non-linear layers to get aspect and opinion representations, and a Graph Attention layer along with a Biaffine scorer to determine the sentiment dependency between each aspect-opinion pair. The comparison results show that our proposed framework significantly outperforms the baselines in Portuguese and is competitive with its counterparts in English.

方面情感三元组提取（Aspect Sentiment Triplet Extraction，简称ASTE）是面向方面基于情感分析（Aspect-Based Sentiment Analysis，简称ABSA）的一个子任务。该任务旨在从句子中提取方面-意见对，并识别与之相关的情感极性。例如，针对句子“宽敞的房间和美味的早餐”，ASTE能够输出三元组T = {(rooms, large, positive), (breakfast, great, positive)}。尽管近期已有多种面向ABSA的方法被提出，但针对葡萄牙语的方案大多仅限于提取方面，而未涉及ASTE任务。本项工作旨在开发一个基于深度学习的框架，以在葡萄牙语中执行方面情感三元组提取任务。该框架采用BERT作为上下文感知的句子编码器，利用多个并行非线性层获取方面和意见表示，并通过图注意力层以及双亲和性评分器来确定每个方面-意见对之间的情感依存关系。对比结果表明，我们提出的框架在葡萄牙语中显著优于基线模型，且与英语中的同类框架具有竞争力。