Attention-based Relational Graph Convolutional Network for Target-Oriented Opinion Words Extraction (2021)

Abstract: Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based sentiment analysis (ABSA). It aims to extract the corresponding opinion words for a given opinion target in a review sentence. Intuitively, the relation between an opinion target and an opinion word mostly relies on syntactics. In this study, we design a directed syntactic dependency graph based on a dependency tree to establish a path from the target to candidate opinions. Subsequently, we propose a novel attention-based relational graph convolutional neural network (ARGCN) to exploit syntactic information over dependency graphs. Moreover, to explicitly extract the corresponding opinion words toward the given opinion target, we effectively encode target information in our model with the target-aware representation. Empirical results demonstrate that our model significantly outperforms all of the existing models on four benchmark datasets. Extensive analysis also demonstrates the effectiveness of each component of our models. Our code is available at https://github.com/wcwowwwww/towe-eacl .

## introduction target-oriented opinion words extraction (towe) @xcite is a subtask of aspect-based sentiment analysis (absa) @xcite . given a review and an opinion target in the sentence, the objective of towe is to extract the corresponding opinion words describing or evaluating the opinion targets from the review. opinion targets are the words or phrases representing features or entities toward which users express their attitudes, whereas opinion words referring to the food is tasty and portion sizes are appropriate. target: food opinion: tasty the food is tasty and portion sizes are appropriate. target: portion size opinion: appropriate figure 1 shows two examples of towe. in the review "the food is tasty and portion sizes are appropriate .", the terms "food" and "portion sizes" are two given opinion targets. towe needs to extract the word "tasty" as the opinion word for the opinion target "food" and the opinion word "appropriate" for the opinion target "portion sizes". therefore, the first challenge is to effectively introduce the opinion target information into our model. @xcite designed the io-bilstm to encode the context before and after the given opinion targets separately to represent the position of the existing opinion targets. @xcite introduced position embeddings based on the relative distance toward opinion targets. however, both studies only introduce parts of target information (the position information of targets). in this paper, we introduce the target-aware representation to fully exploit opinion target information in a concise way, which is especially important when our models are used for real-world reviews. becase towe can be viewed as a syntactic task, a natural solution is analysing the relationship between opinion targets and opinion words by dependency parsing. recently, owing to the great success of graph convolutional networks (gcns) in various fields @xcite @xcite , a few researchers have attempted to encode the syntactic dependency information with gcns to build a robust dependency encoder. for example, gcns over the dependency tree have been exploited to perform semantic role labelling @xcite and named entity recognition @xcite .in addition, several studies explore gcns over a dependency graph to complete the absa task @xcite , @xcite , @xcite , @xcite ). however, it is worth mentioning that towe is defined as a sequence labelling task, and the manner in which gcns are applied to towe effectively is yet to be explored. in this study, we first construct a directed graph based on a dependency tree to be more suitable for towe. subsequently, we propose argcn, which can enhance our model by encoding syntactic information. argcn can be seen as extending the relational graph convolutional networks (r-gcns) @xcite with the distance-aware attention mechanism. argcn can consider the semantic relevance and syntactic relevance between words simultaneously when it propagates information. in addition, sequential information is extremely important for sequence-labelling tasks. therefore, after using multi-layer graph convolutions to encode syntactic information, we feed the syntactic representation to a bidirectional lstm @xcite ) to capture the sequential information. experiments on four benchmark datasets demonstrate that our base model, target-bilstm which is a bilstm with target-aware inputs has a similar or better performance than the state-of-the-art model, although we do not introduce extra external knowledge. in addiction, our full model argcn further improves the performance and significantly outperforms all of the existing models on four benchmark datasets. furthermore, extensive experiments demonstrate the effectiveness and necessity of all components in our full model. to the best of our knowledge, it is the first work on applying gcns to the towe task. the contributions of this paper can be summarized as follows. an empirical study shows it is significant and extensible for the towe task. experimental results show that it significantly outperforms the state-of-the-art model on all datasets of the towe task. ## related work as subtasks of absa, a series of early studies focused on opinion targets extraction, including unsupervised/semi-supervised methods @xcite @xcite and supervised methods @xcite . some recent studies extracted opinion targets and opinion words jointly in a uniform framework and achieved promising results @xcite . however, they did not extract the corresponding relation between opinion targets and opinion words. moreover, studies on extracting paired opinion relations are rare @xcite . because it is important for downstream sentiment analysis and real-world applications, @xcite proposed a new subtask of absa, target-oriented word extraction, aiming to extract the corresponding opinion words for the given opinion targets in a review. they released four benchmark datasets for evaluation, designed a target-fused model, and achieved excellent performance. @xcite adopted transfer learning to transfer latent opinion information from the sentiment analysis model to the towe model. in this study, we also focus on the towe task. since kipf and welling (2016) proposed their gcn with some simplifications on chebnet @xcite , a variety of graph convolutional networks appeared @xcite @xcite and achieved great success in many fields, including computer vision @xcite @xcite , natural language processing @xcite @xcite and even in chemistry @xcite . one of the reasons why gcns work well in several fields is that they can naturally process the graph-structured data to greatly exploit the latent information behind the graph structure. therefore, they are proven to be efficient especially with only a small amount of data @xcite . recently, a few studies have tried applying gcns over the dependency graph to complete some absa tasks. @xcite proposed the cdt to perform gcn over a dependency tree together with contextual representations extracted by bilstm. @xcite introduced dependency relational embedding to @xcite to complete absa with their dregcn. specially, the r-gat-absa @xcite ) is a newly proposed architecture for the absa task. it focuses on the gat @xcite and extends it by introducing relational embedding for calculating relational attention. ## conclusions in this paper, we proposed a target-aware representation to efficiently introduce opinion target information to our towe model. moreover, we proposed argcn by extending the r-gcns with a distance-aware attention mechanism. because the sequential information is essential for such a sequence-labelling task, we captured the sequential information with bilstm after argcn layers and then completed the towe task. empirical results show that our model significantly outperforms all baselines, including state-of-the-art, with large margins, which strongly proves the effectiveness of our model. the extensive analysis also demonstrated the effectiveness and necessity of all components in our model. in addition, we found that gnn model, especially a well-designed gnn model, such as argcn, is suitable for encoding syntactic information. we hope that these findings can be insightful for other researchers in the community.