论文标题
Auto-ABSA：使用辅助句子的跨域方面检测和情感分析
Auto-ABSA: Cross-Domain Aspect Detection and Sentiment Analysis Using Auxiliary Sentences
论文作者
论文摘要
提出变压器后，已经提出了许多预训练的语言模型，并改善了情感分析（SA）任务。在本文中，我们提出了一种使用辅助句子的方法，该句子涉及该句子所包含的方面以帮助情感预测。第一个是方面检测，它使用多光谱检测模型来预测句子的所有方面。将预测的方面和原始句子与情感分析（SA）模型的输入相结合。第二个是进行基于域外的情感分析（ABSA），使用一种数据集进行火车情感分类模型，并使用另一种数据集对其进行验证。最后，我们创建了两个基准，他们分别将任何方面和所有方面都用作情感分类模型的输入。将两个基线的性能与我们的方法进行比较，发现我们的方法确实有意义。
After transformer is proposed, lots of pre-trained language models have been come up with and sentiment analysis (SA) task has been improved. In this paper, we proposed a method that uses an auxiliary sentence about aspects that the sentence contains to help sentiment prediction. The first is aspect detection, which uses a multi-aspects detection model to predict all aspects that the sentence has. Combining the predicted aspects and the original sentence as Sentiment Analysis (SA) model's input. The second is to do out-of-domain aspect-based sentiment analysis(ABSA), train sentiment classification model with one kind of dataset and validate it with another kind of dataset. Finally, we created two baselines, they use no aspect and all aspects as sentiment classification model's input, respectively. Compare two baselines performance to our method, found that our method really makes sense.
