Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading Comprehension (2021)

Abstract: The pivot for the unified Aspect-based Sentiment Analysis (ABSA) is to couple aspect terms with their corresponding opinion terms, which might further derive easier sentiment predictions. In this paper, we investigate the unified ABSA task from the perspective of Machine Reading Comprehension (MRC) by observing that the aspect and the opinion terms can serve as the query and answer in MRC interchangeably. We propose a new paradigm named Role Flipped Machine Reading Comprehension (RF-MRC) to resolve. At its heart, the predicted results of either the Aspect Term Extraction (ATE) or the Opinion Terms Extraction (OTE) are regarded as the queries, respectively, and the matched opinion or aspect terms are considered as answers. The queries and answers can be flipped for multi-hop detection. Finally, every matched aspect-opinion pair is predicted by the sentiment classifier. RF-MRC can solve the ABSA task without any additional data annotation or transformation. Experiments on three widely used benchmarks and a challenging dataset demonstrate the superiority of the proposed framework.

## introduction aspect-based sentiment analysis (absa) aims at detecting opinions towards different targets (also known as aspects) instead of inferring overall sentiment polarity in a given sentence @xcite . it generally consists of three fundamental sub-tasks, namely, aspect terms extraction (ate), opinion terms extraction (ote), and aspect sentiment classification (asc). ate and ote extract aspect and opinion terms from sentences, respectively. and asc predicts the sentiment polarities (i.e., positive, negative, and neutral) towards aspect terms. practically, the heart of asba is to capture the connection between aspect terms and their respective opinion terms, which might make it easier to predict the correct sentiment polarities. such connection is more substantial when multiple aspects with different polarities exist. for example, we illustrate the connection within a sentence shown in figure 1 . the negative polarity of "falafel" can be derived by an aggregation of the relevant opinions "over cooked" and "dried", whereas the positive polarity of "chicken" is oriented by its corresponding opinion word "fine". if the aspect terms and their connected opinion words are mismatched, the prediction may become difficult even incorrect. hence, immense efforts have been dedicated to grasping the relations between aspect terms and their potential corresponding opinion terms. early methods only focused on asc task and relied on given aspect terms. among them, a series of methods designed attention mechanisms @xcite or gating mechanisms @xcite to collect aspectrelated information (e.g., opinion terms) from context. recently, graph neural network over different dependency trees @xcite @xcite was proposed to link aspect terms with interrelated opinion terms more directly. they can account for long-range word dependencies and refrain from identifying contextual words unrelated to aspect terms. despite their effectiveness, these methods will be infeasible if the given aspects are absent. as a result, some researchers proposed to incorporate all sub-tasks in a framework of unified absa. these methods @xcite formulated sub-tasks of absa as sequence labeling tasks. by multifarious interaction mechanisms performed on sentence representations of different sub-tasks, they made the aspect terms come into contact with opinion terms. furthermore, recent researches @xcite put forward to extract (aspect, opinion, sentiment) triples from sentences without given aspect terms. they strive to clarify each aspect-opinion pair for sentiment prediction and needed additional labels of triples compared to the previous unified absa. in this paper, we examine the unified absa from a perspective of machine reading comprehension (mrc). the mrc framework operates on the context, query, and answer triples @xcite , in which the constructed natural language query is asked to the context, and the answer is extracted from the context. by observing that the aspect terms and opinion terms can be naturally characterized as queries and answers, we propose a new paradigm named role flipped machine reading comprehension (rf-mrc) to meet the heart of the unified absa. first, we extract the initial aspect and opinion terms from a given sentence. then either the initial aspect terms or opinion terms are deemed as a query to extract corresponding opinion terms or aspect terms as answers. the roles of query and answer can be flipped to perform a multi-hop question-answering process. in this manner, we can progressively obtain the aspect or opinion terms we need without manually designing queries. meanwhile, the aspect terms could be potentially associated with relevant opinion terms as the multiple question-answering proceeds. furthermore, we propose a matching module to match all the extracted aspects and relevant opinion terms in pairs simultaneously instead of extracting only one aspect-opinion pair at one time, considering a complex sentence may contain multiple aspects with conflict polarities. experiments on three widely used benchmarks and a challenging dataset demonstrate the superiority of the proposed framework. ## conclusion in this paper, we investigate the unified absa from the perspective of mrc and propose a new paradigm named rf-mrc. either extracted aspect terms or opinion terms are constructed as queries, and the related opinion terms and aspect terms are considered as answers. we further design a matching module to match all the extracted aspect terms and relevant opinion terms, and predict the sentiment polarities. experiments on three widely used benchmarks and a challenging dataset demonstrate the superiority of the proposed framework.