A Unified Generative Framework for Aspect-based Sentiment Analysis (2021)

Abstract: Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms. There exist seven subtasks in ABSA. Most studies only focus on the subsets of these subtasks, which leads to various complicated ABSA models while hard to solve these subtasks in a unified framework. In this paper, we redefine every subtask target as a sequence mixed by pointer indexes and sentiment class indexes, which converts all ABSA subtasks into a unified generative formulation. Based on the unified formulation, we exploit the pre-training sequence-to-sequence model BART to solve all ABSA subtasks in an end-to-end framework. Extensive experiments on four ABSA datasets for seven subtasks demonstrate that our framework achieves substantial performance gain and provides a real unified end-to-end solution for the whole ABSA subtasks, which could benefit multiple tasks.

## introduction aspect-based sentiment analysis (absa) is the fine-grained sentiment analysis (sa) task, which aims to identify the aspect term (a), its corresponding sentiment polarity (s), and the opinion term (o). for example, in the sentence "the drinks are always well made and wine selection is fairly priced", the aspect terms are "drinks" and "wine selection", and their sentiment polarities are both "positive", and the opinion terms are "well made" and "fairly priced". based on the combination of the a, s, o, there exist seven subtasks in absa. we summarize these subtasks in figure 1 . specifically, their definitions are as follows: subtask input output task type aspect term extraction(ae) s a 1 , a 2 extraction opinion term extraction(oe) s o 1 , o 2 extraction aspect-level sentiment classification(alsc) s + a 1 s 1 classification s + a 2 s 2 aspect-oriented opinion extraction(aoe) s + a 1 o 1 extraction s + a 2 o 2 aspect term extraction and sentiment classification(aesc) s (a 1 , s 1 ), (a 2 , s 2 ) extraction & classification pair extraction(pair) s (a 1 , o 1 ), (a 2 , o 2 ) extraction triplet extraction(triplet) s (a 1 , o 1 , s 1 ), (a 2 , o 2 , s 2 ) extraction & classification • aspect term extraction(ae): extracting all the aspect terms from a sentence. although these absa subtasks are strongly related, most of the existing work only focus 1∼3 subtasks individually. the following divergences make it difficult to solve all subtasks in a unified framework. and triplet) only take the text sentence as input, while the remained subtasks ( alsc and aoe) take the text and a given aspect term as input. ## methodology although there are two types of tasks among the seven absa subtasks, they can be formulated under a generative framework. in this part, we first introduce our sequential representation for each absa subtask. then we detail our method, which utilizes bart to generate these sequential representations. ## framework analysis to better understand our proposed framework, we conduct analysis experiments on the d 20b dataset @xcite . to validate whether our proposed framework could adapt to the generative absa task, we metric the invalid predictions for the triplet. specifically, since the triplet requires the prediction for-mat like [a s , a e , o s , o e , s p ], it is mandatory that one valid triplet prediction should be in length 5, noted as "5-len", and obviously all end index should be larger than the corresponding start index, noted as "ordered prediction". we calculate number of non-5-len total prediction , referred to as the "invalid size", and the number of non-ordered prediction total 5-len prediction , referred to as the "invalid order". the "invalid token" means the a s is not the start of a token, instead, it is the index of an inside subword. from as shown in ## conclusion this paper summarizes the seven absa subtasks and previous studies, which shows that there exist divergences on all the input, output, and task type sides. previous studies have limitations on handling all these divergences in a unified framework. we propose to convert all the absa subtasks to a unified generative task. we implement the bart to generate the target sequence in an end-to-end process based on the unified task formulation. we conduct massive experiments on public datasets for seven absa subtasks and achieve significant improvements on most datasets. the experimental results demonstrate the effectiveness of our method. our work leads to several promising directions, such as sequence-to-sequence framework on other tasks, and data augmentation.