Efficient Hybrid Generation Framework for Aspect-Based Sentiment Analysis (2023)

Abstract: Aspect-based sentiment analysis (ABSA) has attracted broad attention due to its commercial value. Natural Language Generation-based (NLG) approaches dominate the recent advance in ABSA tasks. However, current NLG practices are inefficient because most of them directly employ an autoregressive generation framework that cannot efficiently generate location information and semantic representations of ABSA targets. In this paper, we propose a novel framework, namely Efficient Hybrid Generation (EHG) to revolutionize traditions. Specifically, we leverage an Efficient Hybrid Transformer to generate the location and semantic information of ABSA targets in parallel. Besides, we design a novel global hybrid loss function in combination with bipartite matching to achieve end-to-end model training. Extensive experiments demonstrate that our proposed EHG framework outperforms current state-of-the-art methods in almost all cases and outperforms existing NLG-based methods in terms of inference efficiency.

## introduction aspect-based sentiment analysis (absa) enjoys broad commercial value, e.g., analyzing customer's opinions through review data to improve products. absa consists of four basic sentiment elements, including aspect term, opinion term, aspect category and sentiment polarity. illustrated in fig. 1 , the aspect terms a 1 , a 2 in the sentence belong to the aspect categories c 1 , c 2 , respectively, and their corresponding opinion terms are o 1 , o 2 , with sentiment polarity s 1 , s 2 . for simplicity, we name the aspect term and opinion term as sentiment entities (se), and the aspect category and sentiment polarity as sentiment abstractions (sa). the majority of absa subtasks utilize sentences as input and the combination of se and sa as output, so we define the output set of all absa subtasks as sentiment tuples (st). studied in this work. natural language understanding (nlu) based approaches were utilized in early research for modeling absa problems. the two stage pipeline, i.e., using a sequence tagging model to extract se, then using a classification model to distinguish sa @xcite @xcite , ignored the semantics of the labels and propagated error in a not end-to-end manner. besides, unique end-to-end nlu variants were proposed to enhance the information interaction between elements within st, however, these approaches suffered from complex model design and were not generic for different absa subtasks. natural language generation (nlg) based approaches model the absa tasks uniformly as sequence-to-sequence generation tasks @xcite @xcite and have dominated the recent advance in absa tasks. specifically, the sequenceto-sequence generation framework can adapt to multiple absa subtasks simultaneously without additional architectural design, and the autoregressive generation mode @xcite ) can naturally include information interaction of elements within st. despite the superior performance of nlgbased approaches, the autoregressive generation also renders nlg-based methods time inefficient in generating multiple sets of sts. besides, existing nlg-based methods fail to adequately coordinate the semantic representation of sts and the positioning of ses when generating results. in this paper, we propose an efficient hybrid generation framework ehg to generate semantic representations of multiple sts in parallel and simultaneously output the location information of ses corresponding to each st. ehg is composed of two modules: efficient hybrid transformer (eht) and entity correction module (ecm). for eht, inspired by detr @xcite , we use a fixed number of object queries to detect sts in sentences in parallel and locate the corresponding se. further, we pass object queries that detect st (obtained by filter or matcher), through the semi-autoregressive generator to generate semantic representations of the corresponding st. in addition we propose a novel global hybrid loss function in combination with bipartite matching named hybrid hungarian loss, to achieve end-to-end model training. for ecm, we normalize the location information of the corresponding ses based on the input text and the semantic representation of each st generated. the contributions of this paper are summarized as below. ## related work in this section, we summarize the existing nlubased and nlg-based approaches for aspect-based sentiment analysis. ## methodology our proposed efficient hybrid generation framework is depicted in detail in fig. 2 . since the output of all six absa subtasks contains the part of ses, they can be described under a unified framework. to facilitate the description, this section expands on aesc as an example, first introducing the task definition, and then efficient hybrid transformer and entity correction module, are described in detail. ## conclusion in this paper, we propose a novel efficient hybrid generation framework to model various absa subtasks. specifically, we propose an efficient hybrid transformer to generate sentiment tuples in parallel and simultaneously locate the corresponding sentiment entities. moreover, we propose a hybrid hungarian loss to achieve end-to-end model training. finally, we propose an entity correction module to normalize the location information of sentiment entities. extensive experiments demonstrate that our proposed ehg framework achieves competitive results while maintaining efficient inference.