Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis (2021)

Abstract: Both the issues of data deficiencies and semantic consistency are important for data augmentation. Most of previous methods address the first issue, but ignore the second one. In the cases of aspect-based sentiment analysis, violation of the above issues may change the aspect and sentiment polarity. In this paper, we propose a semantics-preservation data augmentation approach by considering the importance of each word in a textual sequence according to the related aspects and sentiments. We then substitute the unimportant tokens with two replacement strategies without altering the aspect-level polarity. Our approach is evaluated on several publicly available sentiment analysis datasets and the real-world stock price/risk movement prediction scenarios. Experimental results show that our methodology achieves better performances in all datasets.

## introduction data annotation, the first step of most artificial intelligence researches, often takes lots of time and is very expensive. many studies propose methodologies for augmenting data based on a few annotations @xcite @xcite @xcite to reduce the cost of annotation. however, most of them encounter a problem-hardly ensuring readability and semantic coherence. to overcome these problems, we introduce a novel method, selective perturbed masking (spm), to measure the importance of each word in a textual sentence, and further replace the unimportant word with pre-trained language models. in this way, the semantics of a given instance will be preserved, and the training set will be enlarged with various auto-generated data. while testing the data augmentation methods, classification tasks are often adopted. sentiment analysis is one of the well-known classification tasks. to mining more fine-grained information, aspect-based sentiment analysis (absa) is proposed. absa not only aims at detecting the sentiment polarity but also attempts to mine the analysis aspect. it can be extended to subtasks of aspect category sentiment classification (acsc), aspect term sentiment classification (atsc), and aspect term extraction (ate). for example, the answers to these tasks to the given sentence "the staff was so kind" are (service, positive), (staff, positive), and "staff", respectively. these tasks are commonly taken as examples for evaluating the performance of augmentation methods. in this paper, we explore both sentiment analysis and all subtasks in absa to show the usefulness of the proposed method in semantics preservation. in addition to probe on sentiment analysis and absa tasks, we also experiment on stock price and risk movement prediction tasks. these experiments have always been considered as real-world sentiment analysis application scenarios @xcite . the experimental results on all kinds of datasets support the usefulness of the proposed data augmentation method, and also indicate that the improvement of using the proposed method is larger than that of using other augmentation methods. our main contributions are summarized as follows: ## methods this section describes how we combine an auxiliary sentence with spm in detail to decide which words are unimportant to the aspect or sentiment of a given instance. after deciding the unimportant words, we use the proposed token replacement methods to construct the augmented sentences, in which aspect and sentiment are not altered. ## conclusion in this paper, we present a controllable augmentation for absa, which is controllable to generate reasonable reviews without converting aspect-level polarity. we propose spm to measure the impact of the related words on deciding specific aspect and sentiment, and adopt two replacement strategies to absa tasks. experimental results show the effectiveness and robustness of our approaches. additionally, the exploration in the financial application scenario also supports the usefulness of the proposed method. in the future, we plan to use the proposed method for data augmentation on longer documents and for generating the training instances of low-resource languages.