# Integration of Multi-Branch GCNs Enhancing Aspect Sentiment Triplet Extraction
## 1 Introduction  
Recently, a tremendous advance has been achieved in the development of social media platforms, which largely encourage people to express their emotional states online [1, 2]. Furthermore, it has become popular to publish users' comments or opinions about services and products on specific electronic platforms in a timely manner. These perspectives expressed directly by consumers are extremely important for merchants to improve their service while in a dealing. Thus, how to extract the exact aspect terms, opinion terms, and their corresponding sentiment from a specific sentence is a significant Natural Language Processing (NLP) subtask [3, 4, 5]. The recent developing task, Aspect-Based SentimentAnalysis (ABSA), aims to mine the explicit or implicit sentiment information about the opinion terms with regard to the specific aspect terms, which implements sentiment analysis about consumers' reviews effectively. Generally, the ABSA task contains seven fundamental subtasks (Figure 1), which are Aspect Term Extraction (ATE) [6], Aspect Term Extraction and Sentiment Classification (AESC) [7], Opinion Term Extraction (OTE) [6], Aspect-Based Sentiment Classification (ABSC) [8], Aspect-Oriented Opinion Term Extraction (AOE) [9], Pair Extraction (PE) [10], and Aspect Sentiment Triplets Extraction (ASTE) [11]. In particular, as the fine-grained subtask in ABSA, the ASTE task takes aspect terms, opinion terms, and sentiment polarities into consideration simultaneously, which is challenging but significant. For example, as shown in Figure 1, the review \"The food is good, but the service is terrible\". contains two triplets, (food, good, positive) and (service, terrible, negative). Unlike the other subtasks, such triplets extracted by the ASTE task can better reflect multiple emotional factors (aspect, opinion, sentiment) from the user reviews and are more proper for practical application scenarios. In previous studies, the pipeline manner is widely applied in the approaches to ASTE. [PERSON] et al. [12] first introduced the ASTE task and extracted the triplet {aspect, opinion, sentiment} via utilizing a pipeline method, which contains a two-stage framework. The first stage provided predictions about aspect, opinion, and sentiment, respectively. Furthermore, the second stage was designed to pair up the predictions achieved from the first stage and output triplets. However, the interactions among them were totally ignored, and the potential error was propagated between these two stages [13; 14]. To take the dependencies among the multiple subtasks into consideration, the multi-turn machine reading comprehension (MRC) manner [15; 16] was utilized to jointly train multiple subtasks together, and it has achieved significant results. In addition, the fashion of end-to-end [17; 18] also attracts many researchers' attentions, which is constructed based on the new tagging scheme. Although the paradigm of the framework is important to enhance the performance of the ASTE task, the effective utilization of various linguistic relations between words is also decisive to the task's success [19]. Specifically, the syntactic dependency tree is widely used to present the structure of a sentence, which tends to depict the syntactic relations among words. [PERSON] et al. [20] adopted the dependency tree as the support to capture relations between aspect and opinion terms. Furthermore, the work [21] directly employed an interactive attention mechanism to integrate syntactic and semantic relations between words. In addition, the contribution of part-of-speech categories to ASTE is also noticed, which straightly impacts the semantic representation of sentences. Except for the  
Figure 1: Example of the task categories in (ABSA) Aspect-Based Sentiment Analysis. dependency tree, relative position also largely influences the expression of the sentence. [PERSON] et al. [22] applied a position-aware tagging scheme to mark the relative position between words in a sentence.