论文标题
对基于方面情感分析的对抗性培训
Adversarial Training for Aspect-Based Sentiment Analysis with BERT
论文作者
论文摘要
基于方面的情感分析（ABSA）涉及情感及其目标的提取。为了帮助神经网络更好地概括为了帮助神经网络收集标签的数据可能是费力且耗时的。作为替代方案，可以通过在嵌入空间中进行的对抗过程人为地生成与现实世界实例相似的数据。尽管这些示例不是真实的句子，但它们已被证明是一种正规化方法，可以使神经网络更加稳健。在这项工作中，我们采用了对抗性训练，这是Goodfellow等人提出的。 （2014年），Xu等人提出的经过训练的BERT（BERT-PT）语言模型。 （2019）关于情感分析中方面提取和方面情感分类的两个主要任务。在通过消融研究改善了训练后BERT的结果后，我们提出了一种称为Bert对抗训练（BAT）的新型建筑，以利用ABSA中的对抗性训练。所提出的模型在这两个任务中的表现都优于训练后的BERT。据我们所知，这是关于ABSA中对抗性培训的第一个研究。
Aspect-Based Sentiment Analysis (ABSA) deals with the extraction of sentiments and their targets. Collecting labeled data for this task in order to help neural networks generalize better can be laborious and time-consuming. As an alternative, similar data to the real-world examples can be produced artificially through an adversarial process which is carried out in the embedding space. Although these examples are not real sentences, they have been shown to act as a regularization method which can make neural networks more robust. In this work, we apply adversarial training, which was put forward by Goodfellow et al. (2014), to the post-trained BERT (BERT-PT) language model proposed by Xu et al. (2019) on the two major tasks of Aspect Extraction and Aspect Sentiment Classification in sentiment analysis. After improving the results of post-trained BERT by an ablation study, we propose a novel architecture called BERT Adversarial Training (BAT) to utilize adversarial training in ABSA. The proposed model outperforms post-trained BERT in both tasks. To the best of our knowledge, this is the first study on the application of adversarial training in ABSA.
