A Contrastive Cross-Channel Data Augmentation Framework for Aspect-Based Sentiment Analysis (2022)

Abstract: Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, which focuses on detecting the sentiment polarity towards the aspect in a sentence. However, it is always sensitive to the multi-aspect challenge, where features of multiple aspects in a sentence will affect each other. To mitigate this issue, we design a novel training framework, called Contrastive Cross-Channel Data Augmentation (C3 DA), which leverages an in-domain generator to construct more multi-aspect samples and then boosts the robustness of ABSA models via contrastive learning on these generated data. In practice, given a generative pretrained language model and some limited ABSA labeled data, we first employ some parameter-efficient approaches to perform the in-domain fine-tuning. Then, the obtained in-domain generator is used to generate the synthetic sentences from two channels, i.e., Aspect Augmentation Channel and Polarity Augmentation Channel, which generate the sentence condition on a given aspect and polarity respectively. Specifically, our C3 DA performs the sentence generation in a cross-channel manner to obtain more sentences, and proposes an Entropy-Minimization Filter to filter low-quality generated samples. Extensive experiments show that our C3 DA can outperform those baselines without any augmentations by about 1% on accuracy and Macro- F1. Code and data are released in https://github.com/wangbing1416/C3DA .

## introduction sentiment analysis (sa) is a typical natural language understanding (nlu) task to predict the sentence-level sentiment polarities @xcite . however, since a single review sentence always exists multiple polarities, aspectbased sentiment analysis (absa), a fine-grained sentiment analysis task @xcite @xcite @xcite , is further introduced to detect the sentiment polarities towards given aspects (entities) in a review sentence. gathering a review "while the ambiance and atmosphere were great, the food and service could have been a lot better.", the sentiment polarities of "ambiance" and "atmosphere" are positive and we get negative polarity for aspects "food" and "service". it is crucial to capture the aspect-specific contextual features for an absa model. unfortunately, as shown in the aforementioned example, the multiaspect challenge that there are multiple aspects in a sentence always deteriorates the model's generalization, especially when the expressed polarities of multiple aspect words in a sentence are opposite. meanwhile, since absa is a low-resource task, it is hard to train a robust model under fewer samples. some existing works focused on the multi-aspect challenge, such as @xcite ; @xcite in which novel model structures were designed to capture aspect-specific sentiment information. additionally, @xcite manually collected a large-scale high-quality multi-aspect multisentiment (mams) dataset. undoubtedly, the datacentric mams is an effective approach to tackle the multi-aspect problem, but the human-annotated and non-expandable limitations still hinder the robust training for absa models. therefore, given limited labeled data, it is critical to investigate how to collect more in-domain multi-aspect samples automatically. on the other hand, capturing the aspect-specific information from multi-aspect sentences is also necessary for robust absa models. in response to the aforementioned problems, we propose a novel training framework, namely contrastive cross-channel data augmentation (c 3 da), to generate more in-domain multi-aspect samples and train robust absa models based on these generated data. firstly, inspired by successful generative pretraining models @xcite , we employ an representative pretraining models t5 @xcite as the multi-aspect data generator. in practice, due to the limited absa labeled data, it is hard to fine-tuning the entire t5 model effectively. thus, we further introduce some parameter-efficient methods, e.g. prompt @xcite , prefix @xcite and lora @xcite , to tune the t5 generator. in this way, a domain -specific generator is obtained and we can apply it to collect more in-domain multi-aspect samples. to be more specific, there are two channels to generate expected multi-aspect samples in our framework, i.e. aspect augmentation channel (aac) and polarity augmentation channel (pac). in the aac, the generator is encouraged to generate the synthetic sentence towards the given sentence and aspect, while in the pac, the synthetic sentence towards the given sentence and polarity is obtained. to further boost the generated samples, we attempt to collect the multi-aspect samples in a cross-channel manner. specifically, in the first generation, the source sentence is fed to the double channels and obtain an aspect-specific sentence and a polarity-inverted sentence respectively. in the second generation, both generated sentences are injected to the another channel to get the ultimate sentences. finally, we propose an entropy-minimization filter (emf) to filter some low-quality generated samples. and a contrastive training objective that can draw away the different aspect's embeddings in a sentence is also leveraged to alleviate the multi-aspect problem and help train the final robust absa model. we conduct sufficient experiments on three popular absa datasets, i.e. restaurant, laptop @xcite and twitter @xcite , to prove that our c 3 da framework can boost the model's robustness and predictive performance, and outperform other nlp data augmentation strategies. specifically, with the help of our c 3 da, roberta-based models can achieve averaged performance improvement 1.28% and 1.62% in terms of accuracy and macro-f1 respectively, while the improvements of bert-based models are also over 0.87% and 1.10%. some in-depth discussions and case studies are also executed. contributions of this paper are threefold: ## conclusion in our work, we focus on solving the multi-aspect problem in the absa task. to address this challenge, we propose a data-centric training framework and design a novel c 3 da method to implement data augmentation. the proposed framework conduct both supervised training and contrastive training on augmented samples. the key idea of c 3 da is to generate some sentences that express the opposite sentiment polarity from the source sentences. extensive experiments on widely-used benchmarks demonstrate that our framework can effectively improve the prediction model's robustness and predictive performance. in the future, it will be interesting to apply our c 3 da framework to improve other fine-grained natural language understanding tasks. qihuang zhong, liang ding, juhua liu, bo du, and dacheng tao. 2022b. panda: prompt transfer meets knowledge distillation for efficient model adaptation. corr, abs/2208.10160. deyu zhou, jianan wang, linhai zhang, and yulan he. 2021. implicit sentiment analysis with eventcentered text representation. in conference on empirical methods in natural language processing, pages 6884-6893.