Self Question-answering: Aspect Sentiment Triplet Extraction via a Multi- MRC Framework based on Rethink Mechanism (2023)

Abstract: “The purpose of Aspect Sentiment Triplet Extraction (ASTE) is to extract a triplet, including thetarget or aspect, its associated sentiment, and related opinion terms that explain the underlyingcause of the sentiment. Some recent studies fail to capture the strong interdependence betweenATE and OTE, while others fail to effectively introduce the relationship between aspects andopinions into sentiment classification tasks. To solve these problems, we construct a multi-roundmachine reading comprehension framework based on a rethink mechanism to solve ASTE tasksefficiently. The rethink mechanism allows the framework to model complex relationships be-tween entities, and exclusive classifiers and probability generation algorithms can reduce queryconflicts and unilateral drops in probability. Besides, the multi-round structure can fuse explicitsemantic information flow between aspect, opinion and sentiment. Extensive experiments showthat the proposed model achieves the most advanced effect and can be effectively applied toASTE tasks.”

## introduction aspect-based sentiment analysis (absa) is a fine-grained task @xcite . its purpose is to detect the sentiments of different entities rather than infer the overall sentiment of sentences. as shown in figure 1 , researchers proposed many subtasks of absa, such as aspect term extraction (ate) @xcite , opinion term extraction (ote) @xcite , aspect-based sentiment classification (absc) @xcite , aspect-oriented opinion extraction (aoe) @xcite , etc. aspect terms refer to words or phrases that describe the attributes or characteristics of an entity. opinion terms refer to words or phrases that express the corresponding attitudes of the aspect terms. ate and ote aim to extract aspects and opinions from sentences, respectively. for absc, given a sentence and an aspect within the sentence, it is possible to predict the sentiment (positive, neutral, or negative) associated with that aspect. in the sentence "the service is good, but the food is not so great", ate extracts "service" and "food", and ote extracts "good" and "not so great". absc predicts the sentiment polarity of "service" and "food" as positive and negative, respectively. however, these studies focus on individual tasks respectively while neglecting their interdependencies. recent studies have focused on joint tasks to explore the interactions among different tasks. figure 1 provides examples of aspect term extraction and sentiment co-classification (aesc) as well as aspect-opinion pair extraction(pair). however, these subtasks still cannot tell a complete story. hence aspect sentiment triplet extraction (aste) was introduced. the purpose of aste is to extract aspect terms, related opinion terms, and sentiment polarities for each aspect simultaneously. aste has two advantages: first, opinions can enhance the expressiveness of the model, helping to determine the sentiment of the aspects better; second, the sentiment dependency between aspects and opinions can narrow the gap of sentiment decision-making, further improving the interpretability of the model. peng @xcite proposed the first solution for aste, which jointly extracts aspect-sentiment pairs and opinions using two sequence taggers. sentiment is attached to aspects through a unified tagging figure 1 : illustration of absa subtasks process, and then an exclusive classifier is used to pair the extracted aspect-sentiment pairs with opinions. while this method achieved significant results, there are also some issues. firstly, the model has low computational efficiency because its framework involves two stages and requires training three independent models. secondly, the model does not fully recognize the relationship between ate and ote, and does not effectively utilize the correspondence between aspect terms and opinion terms. thirdly, the correspondence between aspect and opinion expressions can be very complex, involving various relationships such as one-to-many, many-to-one, overlapping, and nesting, which makes it difficult for the model to flexibly and accurately detect these relationships. therefore, we take the solution to the above problems as our challenge. to address the first problem mentioned above, inspired by @xcite , this paper proposes an improved multi-round mrc framework (r-mmrc) with a rethink mechanism to elegantly identify aste within a unified framework. to address the second problem, we decompose the aste into multiple rounds and introduce prior knowledge from the previous round to the current round, which effectively learns the associations between different subtasks. in the first round, we design static queries to extract the first entity of each aspect-opinion pair. in the second round, we design dynamic queries to identify the second entity of each aspect-opinion pair based on the previously extracted entity. in the third round, we design a dynamic sentiment query to predict the corresponding sentiment polarity based on the aspectopinion pairs obtained in the previous round. in each step, the manually designed static and dynamic queries fully utilize the sentence's explicit semantic information to improve the extraction or classification performance. based on these steps, we can flexibly capture complex relationships between entities, effectively mine the connection between ate and ote, and use these relationships to guide sentiment classification. to address the third issue, inspired by human two-stage reading behaviour @xcite , we introduce a rethink mechanism to validate candidate aspect-opinion pairs further, enhance the information flow between aspects and opinions, and improve overall performance. our contributions are summarized as follows: ## related work we present related work in two parts, including various subtasks of aspect-based sentiment analysis and machine reading comprehension. ## conclusion in this paper , we investigate aste task and propose an improved multi-round mrc framework with a rethink mechanism(r-mmrc). this framework sequentially extracts aspect-sentiment pairs and performs sentiment classification, which can handle complex correspondences between aspects, opinions, and sentiments. in each round, explicit semantic information can be effectively utilized. additionally, the rethink mechanism models the bidirectional information flow to verify each candidate aspect-opinion pair, effectively utilizing the corresponding relationship between entities. exclusive classifiers avoid interference between different queries, and probability generation algorithms further improve prediction performance. the experimental results demonstrate the effectiveness of the r-mmrc framework, further improving the overall performance of the system. more importantly, our model can serve as a general framework to address various tasks of absa. however, our model still suffers from the issue of high computational cost, and we hope to compress the model in the future to make it more lightweight. c c l 2 0 2 3