Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis (2021)

Abstract: Existing works for aspect-based sentiment analysis (ABSA) have adopted a unified approach, which allows the interactive relations among subtasks. However, we observe that these methods tend to predict polarities based on the literal meaning of aspect and opinion terms and mainly consider relations implicitly among subtasks at the word level. In addition, identifying multiple aspect–opinion pairs with their polarities is much more challenging. Therefore, a comprehensive understanding of contextual information w.r.t. the aspect and opinion are further required in ABSA. In this paper, we propose Deep Contextualized Relation-Aware Network (DCRAN), which allows interactive relations among subtasks with deep contextual information based on two modules (i.e., Aspect and Opinion Propagation and Explicit Self-Supervised Strategies). Especially, we design novel self-supervised strategies for ABSA, which have strengths in dealing with multiple aspects. Experimental results show that DCRAN significantly outperforms previous state-of-the-art methods by large margins on three widely used benchmarks.

## introduction aspect-based sentiment analysis (absa) is a task of identifying the sentiment polarity of associated aspect terms in a sentence. generally, absa is composed of three subtasks, 1) aspect term extraction (ate), 2) opinion term extraction (ote), and 3) aspect-based sentiment classification (asc). given the sentence "food is good, but service is dreadful.", ate aims to identify two-aspect terms "food" and "service", and ote aims to determine two-opinion terms "good" and "dreadful". then, @xcite ) that we reimplement. all the results are based on bert base model for a fair comparison. the polarity labels pos, neu, and neg, denote positive, neutral, and negative, respectively. asc assigns a sentiment polarity of each aspect: "food (positive)" and "service (negative)". existing works for absa have adopted a twostep approach, which considers each subtask separately @xcite . however, most recently, unified approaches have achieved significant performance improvements in absa task. @xcite focused on modeling the interactions between aspect terms and chen and qian (2020) exploited dyadic and triadic relations between subtasks (i.e., ate, ote, asc). despite the impressive results, their methods have two limitations. first, they only consider relations among subtasks at the word level and do not explicitly utilize contextualized information of the whole sequence. for example, e1 in to address the aforementioned issues, we propose deep contextualized relation-aware network (dcran) for absa. dcran not only implicitly allows interactive relations among the subtasks of absa, but also explicitly considers their relations by using contextual information. our main contributions are as follows: 1) we design aspect and opinion propagation decoder so that the model has a comprehensive understanding of the whole context, and thus it results in better prediction of the polarity. 2) we propose novel selfsupervised strategies for absa, which are highly effective in dealing with multiple aspects and considering deep contextualized information with the aspect and opinion terms. to the best of our knowledge, it is the first attempt to design explicit selfsupervised methods for absa. 3) experimental results demonstrate that dcran significantly outperforms previous state-of-the-art methods on three widely used benchmarks. ## conclusion in this paper, we proposed the deep contextualized relation-aware network (dcran) for aspectbased sentiment analysis. dcran allows interaction between subtasks implicitly in a more effective manner and two explicit self-supervised strategies for deep context-and relation-aware learning. we obtained the new state-of-the-art results on three widely used benchmarks. whw @xcite ) is a unified two-stage framework to extract (aspect, opinion, polarity) triples as a result of ate, ote, and asc. iktn @xcite ) is an iterative knowledge transfer network for absa considering the semantic correlations among the ate, ote, and asc. span @xcite is a pipeline approach to solve ate and asc using bert large . it uses a multi-target extractor for ate and a polarity classifier for asc.