Generative Data Augmentation for Aspect Sentiment Quad Prediction (2023)

Abstract: Aspect sentiment quad prediction (ASQP) analyzes the aspect terms, opinion terms, sentiment polarity, and aspect categories in a text. One challenge in this task is the scarcity of data owing to the high annotation cost. Data augmentation techniques are commonly used to address this issue. However, existing approaches simply rewrite texts in the training data, restricting the semantic diversity of the generated data and impairing the quality due to the inconsistency between text and quads. To address these limitations, we augment quads and train a quads-to-text model to generate corresponding texts. Furthermore, we designed novel strategies to filter out low-quality data and balance the sample difficulty distribution of the augmented dataset. Empirical studies on two ASQP datasets demonstrate that our method outperforms other data augmentation methods and achieves state-of-the-art performance on the benchmarks. The source code will be released upon acceptance.

## introduction aspect-based sentiment analysis (absa) aims to mine opinions expressed regarding specific aspects of a given text. recently, @xcite proposed a challenging compound subtask of absa called aspect sentiment quad prediction (asqp), which predicts four kinds of elements (aspect category, aspect term, opinion term, sentiment polarity) as quadruplets (quads). a single text may contain multiple quads. for example, the text "the pizza is delicious but expensive." mentions one aspect term (pizza) and two opinion terms (delicious and expensive). because these two opinions are related to the same aspect, the text includes two quads: (taste, pizza, delicious, positive) and (price, pizza, expensive, negative). traditional methods @xcite @xcite address such compound sub-it is one the nicest outdoor restaurants i have ever seen in ny. it is one of the most beautiful outdoor restaurants i have ever seen in ny. it is one the nicest restaurants i have ever seen in ny. ## effects on aste task we conduct experiments on the aste task to verify that our method is also effective on other absa subtasks. we compare our method with strong previous work. ## conclusion in this paper, we have proposed a new approach to tackle the problem of data scarcity in the asqp task. to address this challenge, we present a generative data augmentation method based on a pre-trained quads-to-text model. our method generates new parallel data by synthesizing a large number of quads from the training dataset and generating corresponding pseudo texts. moreover, we propose a data filtering strategy to remove low-quality generated data and a measurement to balance the difficulty of augmented samples. our empirical studies on two asqp datasets have demonstrated the superiority of our method compared to other data augmentation methods and the effectiveness of each component in our method. our approach not only is an innovative solution to the problem of data scarcity in asqp, but also provides a potential direction for future work in other related fields, such as relation extraction and event extraction.