# Integration of Multi-Branch GCNs Enhancing Aspect Sentiment Triplet Extraction
## 1 Introduction
Furthermore, the semantic features in this work are represented by Long Short-term Memory (LSTM) with the pre-trained Glove, which cannot handle contextual ambiguity comprehensively. Moreover, the tree-based distance and relative position distance of each word pair in the sentence also contribute a lot to the improvement in the ASTE task [23], and the utilization of Bidirectional Encoder Representation from Transformers (BERT) can largely enhance the feature representation from the semantic perspective. However, although significant progress has been achieved by previous studies, there are still remaining limitations: the effective optimization of semantic features is not enough, and the powerful utilization of multi-type textual features is unsolved yet. To address these two problems, motivated by the impressive performance achieved by BERT, we propose a novel BERT- and Graph Convolutional Network-based (GCN-based) model Multi-branch Graph Convolutional Network (MBGCN) for the ASTE task. In detail, in our model, to evacuate the potential capability of BERT and obtain a more exquisite contextual representation, a structure-biased BERT [24] is firstly utilized as the semantic feature encoder. Subsequently, depending on the generated representations, aspect-oriented and opinion-oriented feature maps are extracted by two multi-layer perceptions (MLP). Then, before incorporating other relations of words, a biaffine attention module is applied to unify the aspect-oriented and opinion-oriented semantic features effectively. Unlike fusing textual features via a single GCN, an MBGCN employs four branch GCNs to integrate semantic representation with syntactic dependency type, part-of-speech combination, tree-based distance, and relative position distance among each word pair, respectively. Through the complementary of these four branches, a more precise textual representation is achieved. Finally, a shallow interaction strategy is designed to complete the work of information fusion before the triplet decoding layer. To validate the effectiveness of the MBGCN, a series of experiments are conducted on four widely used and available datasets. The experimental results prove that MBGCNs can efficiently deal with the complex relations among sentences and outperform the state-of-the-art (SOTA) ASTE approaches. The main contributions of this work can be summarized as follows:  
* We propose a framework MBGCN to extract the aspect, opinion, and sentiment triplet from review sentences in an end-to-end fashion, which can avoid error propagation among different subtasks;
* We utilize a structure-biased BERT to improve the ability to extract abundant contextual information, which provides rich textual features for subsequent task-oriented operations;
* Our proposed MBGCN adopts four branch GCNs to integrate the semantic feature with four types of linguistic relations, including syntactic dependency type, part-of-speech combination, tree-based distance, and relative position distance of each word pair. Furthermore, a shallow interaction layer is introduced to output the final textual representation;
* The extensive experiments conducted on multiple ASTE datasets prove that the proposed MBGCN outperforms the mentioned SOTA baselines. The remainder of this article is organized as follows. In Section 2, we present a brief overview of the development of ABSA, previous research about ASTE, and the application of GCNs. The proposed framework MBGCN is introduced in detail in Section 3. In Section 4, we provide detailed experimental studies and performance analyses. Finally, Section 5 provides a conclusion of this study and an outlook for future work.