# Integration of Multi-Branch GCNs Enhancing Aspect Sentiment Triplet Extraction
## 5 Conclusions  
In this work, we propose an end-to-end model MBGCN for the ASTE task, which processes Aspect Term Extraction, Opinion Term Extraction, and sentiment polarity prediction in a sentence, simultaneously. For modeling the textual semantic feature more accurately, an optimized attention module is inserted into BERT, namely structure-biased BERT, which is employed to enhance the representation of the specific sentence. In addition, \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Versions**} & \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Restaurant14**} & \multicolumn{3}{c}{**Laptop14**} & \multicolumn{3}{c}{**Restaurant15**} & \multicolumn{3}{c}{**Restaurant16**} \\ \cline{3-13}  & & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{2}{*}{\(\mathcal{V}_{1}\)} & MBGCN & 72.89 & 71.79 & **72.33** & 57.30 & 57.62 & 57.46 & 60.76 & 58.42 & 59.57 & 71.68 & 69.22 & **70.43** \\  & \(w\)/ Prompts & 73.27 & 70.18 & 71.69 & 56.35 & 59.45 & **57.86** & 58.32 & 64.71 & **61.35** & 64.11 & 68.64 & 66.30 \\ \hline \multirow{2}{*}{\(\mathcal{V}_{2}\)} & MBGCN & 67.92 & 75.18 & 71.37 & 59.96 & 57.86 & **58.89** & 62.25 & 63.92 & 63.07 & 63.76 & 71.35 & 67.34 \\  & \(w\)/ Prompts & 73.63 & 71.31 & **72.46** & 58.93 & 56.75 & 57.82 & 64.68 & 62.68 & **63.67** & 65.96 & 72.90 & **69.26** \\ \hline \hline \end{tabular} Note: The “w/” denotes the abbreviation of with.  
\end{table} 
Table 6: The contribution of prompt learning to MBGCN for ASTE task on \(\mathcal{V}_{1}\) and \(\mathcal{V}_{2}\). Figure 7: Attention distribution on each word in samples for case study. to emphasize the key features in the generated representation, biaffine attention is utilized to absorb the crucial components from both aspect-oriented and opinion-oriented feature maps. Furthermore, a novel fusion architecture with a multi-branch GCN is proposed to integrate the semantic feature with the linguistic feature. In this part, through each branch GCN, attentive semantic representation is integrated with syntactic dependency types, part-of-speech combination, relative positive distance, and tree-based distance, respectively. Eventually, four branch features are synthesized as an entirety via a designed shallow interaction layer. To validate the effectiveness of our proposed model, we conduct extensive experiments on the benchmark datasets, and the results show that the MBGCN achieves SOTA performances. Although outstanding performances were achieved by our proposed MBGCN, several limitations still exist, which are the working aims of our future study. First, the working mechanism of prompt learning should be optimized to be more proper for our current task. Second, a more robust integration strategy is essential in our future study for feature fusion. Conceptualization, X.S., M.H. and F.R. ; methodology, X.S. and F.R. ; validation, X.S. and M.H. ; formal analysis, X.S. ; investigation, X.S., P.S. and J.Y. ; writing--original draft preparation, X.S. ; writing--review and editing, X.S., J.D. and F.R. ; visualization, X.S. and M.H. ; supervision, F.R. and M.H. ; project administration, F.R. and M.H. ; funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China under Grant 62176084 and Grant 62176083, and in part by the Fundamental Research Funds for the Central Universities of China under Grant PA2022 GDSK0066 and Grant PA2022 GDSK0068. Not applicable. The ASTE-data-V1 dataset is publicly available at [[https://github.com/NJUNLP/GTS](https://github.com/NJUNLP/GTS)]([https://github.com/NJUNLP/GTS](https://github.com/NJUNLP/GTS)) (accessed on 10 October 2022).