Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation (2022)

Abstract: Recently, aspect sentiment quad prediction (ASQP) has become a popular task in the field of aspect-level sentiment analysis. Previous work utilizes a predefined template to paraphrase the original sentence into a structure target sequence, which can be easily decoded as quadruplets of the form (aspect category, aspect term, opinion term, sentiment polarity). The template involves the four elements in a fixed order. However, we observe that this solution contradicts with the order-free property of the ASQP task, since there is no need to fix the template order as long as the quadruplet is extracted correctly. Inspired by the observation, we study the effects of template orders and find that some orders help the generative model achieve better performance. It is hypothesized that different orders provide various views of the quadruplet. Therefore, we propose a simple but effective method to identify the most proper orders, and further combine multiple proper templates as data augmentation to improve the ASQP task. Specifically, we use the pre-trained language model to select the orders with minimal entropy. By fine-tuning the pre-trained language model with these template orders, our approach improves the performance of quad prediction, and outperforms state-of-the-art methods significantly in low-resource settings.

## introduction the aspect sentiment quad prediction (asqp) task, aiming to extract aspect quadruplets from a review sentence, becomes popular recently @xcite . the quadruplet consists of four sentiment elements: 1) aspect category (ac) indicating the aspect class; 2) aspect term (at) which is the specific aspect description; 3) opinion term the restaurant is clean. ambience general is great because restaurant is clean ## a pilot experiment as eq. ( 1 ) displayed, this template forms a fixed order of four elements. our curiosity is whether the quadruplet's order affects the performance of sequence-to-sequence learning. therefore, we conduct a pre-experiment. by only concatenating with commas, four elements can also be transformed into a target sequence. the orders can be switched in a more flexible way, compared with eq. ( 1 ). there will be 4! = 24 permutations. during inference, quadruplets can be recovered by splitting them with commas. based on the pre-experimental results, we have the following observations. template order affects the performances of sequence-to-sequence learning. part of the experimental results on two datasets, i.e. rest15 and rest16, are shown in ## methodology as analyzed in the previous section, the template order influences the performances of both the quadruplet and its four elements. it is hypothesized that different orders provide various views of the quadruplet. we argue that combining multiple template orders may improve the asqp task via data augmentation. however, using all 24 permutations significantly increases the training time, which is inefficient. therefore, we propose a simple method to select proper template orders by leveraging the nature of the pre-trained language model (i.e. t5). then for the asqp task, these selected orders are utilized to construct the target sequence y to finetune the t5 model. specifically, given an input sentence x and its quadruplets {(ac, at, ot, sp)}, following zhang et al. (2021a) , we map them into semantic values {(x ac , x at , x ot , x sp )}. as shown in figure 3 , our approach is composed of two stages, next which will be introduced in detail. ## conclusion in this work, we study aspect sentiment quad prediction (asqp) from the template order perspective. we hypothesize that different orders provide various views of the quadruplet. in light of this hy-pothesis, a simple but effective method is proposed to identify the most proper orders, and further combine multiple proper templates as data augmentation to improve the asqp task. specifically, we use the pre-trained language model to select the orders with minimal entropy. by fine-tuning the pre-trained model with these template orders, our model achieves state-of-the-art performances.