A Robustly Optimized BMRC for Aspect Sentiment Triplet Extraction (2022)

Abstract: Aspect sentiment triplet extraction (ASTE) is a challenging subtask in aspect-based sentiment analysis. It aims to explore the triplets of aspects, opinions and sentiments with complex correspondence from the context. The bidirectional machine reading comprehension (BMRC), can effectively deal with ASTE task, but several problems remains, such as query conflict and probability unilateral decrease. Therefore, this paper presents a robustly optimized BMRC method by incorporating four improvements. The word segmentation is applied to facilitate the semantic learning. Exclusive classifiers are designed to avoid the interference between different queries. A span matching rule is proposed to select the aspects and opinions that better represent the expectations of the model. The probability generation strategy is also introduced to obtain the predicted probability for aspects, opinions and aspect-opinion pairs. We have conducted extensive experiments on multiple benchmark datasets, where our model achieves the state-of-the-art performance.

## introduction aspect-based sentiment analysis (absa) is an important research area of natural language processing (nlp), which aims to mine fine-grained opinions and sentiments based on a specific aspect. in recent years, it has attracted extensive attention of researchers @xcite . absa includes three basic subtasks: aspect term extraction @xcite @xcite , opinion term extraction @xcite , and aspect level sentiment classification @xcite @xcite . substantial progress has been achieved in recent studies, integrating multiple subtasks into a *corresponding author 1 we make our code publicly available at https:// github.com/itkaven/robmrc more complex task @xcite @xcite . among them, aspect sentiment triplet extraction (aste) @xcite becomes a subject of great interest, which is also the goal of our work. many research efforts have been made @xcite @xcite , for example, using bidirectional machine reading comprehension (bmrc) for aste. it handles the task effectively, but problems still remain. in the structure of bmrc, the shared classifiers may lead to query conflicts based on specific context, thus affecting the model performance. some important strategies are also ignored, such as word segmentation, span matching and probability generation. in this paper, we present a robustly optimized bmrc method for aste. the task is transformed into a machine reading comprehension problem. the complex correspondence between the aspect and opinion is processed through bidirectional query based on specific context. such relationship can be effectively used to make their extraction mutually beneficial, thus facilitating better prediction of various sentiments. in order to deal with the aste task more efficiently, we incorporate the word segmentation and exclusive classifiers, and improve the span matching where the priority rule of the combination of probability and position relationship has been added. we also optimize the generation of probability to avoid its unilateral decrease. our contributions can be summarized as follows: ## methodology in this section, we briefly review the aste task and bmrc model, and then introduce our four improvements in detail. ## experiment in this section, we introduce information about the experiments, including datasets, evaluation metrics, baselines, experimental results, and ablation study. ## conclusion in this paper, we propose several improvements on the basis of bmrc for aste task, which can effectively deal with the complex correspondence among aspect, opinion and sentiment. in order to deal with the problems of the original bmrc, we add exclusive classifiers and three strategies, including word segmentation, span matching and probability generation. the proposed method is expected to handle complex aste task more efficiently. extensive experiments are conducted to demonstrate the advantages of our improvements.