1. An incremental study of this topic based on previous works.
- [Bao IJCAI 2022] Aspect-based Sentiment Analysis with Opinion Tree Generation, IJCAI 2022 (OTG method)
- [Bao ACL Finding 2023] Opinion Tree Parsing for Aspect-based Sentiment Analysis, Findings of ACL 2023
The techniques involved in the proposed framework have been commonly used for ABSA, including graph pre-training, opinion tree generation and so on, and it seems not surprising enough to combine them together. The experimental results only show the performance can be improved, but lack of the explanations of why the performance can be improved.
2. The experimental results are not exactly convincing, by comparing with the main results in [Bao IJCAI 2022] and [Bao ACL Finding 2023]. For example,
- For the scores of OTG method, [Bao ACL Finding 2023] < this paper < [Bao IJCAI 2022]. Note that this is a significant difference, for example, on the Restaurant dataset, for F1 score, [Bao ACL Finding 2023] 0.6040 < this paper 0.6164 < [Bao IJCAI 2022] 0.6283; on the laptop dataset, for F1 score, [Bao ACL Finding 2023] 0.3998 < this paper 0.4394 < [Bao IJCAI 2022] 0.4544
- On the laptop dataset, for F1 score, although the scores of OTG method in this paper 0.4394 < the scores of proposed method in this paper 0.4512; The scores of OTG method in [Bao IJCAI 2022] 0.4544 > the scores of proposed method in this paper 0.4512;
There are also other significant differences on the performance of the baseline methods in these papers.
3. It could be convincing to discuss case studies and error studies to highlight the effectiveness of each proposed component. For example, this paper mentions that the Element-level Graph Pre-training abandons the strategy of capturing the complex structure but focuses directly on the core elements. However, without case study, it is less convincing to figure it out. An example of case study can be found in “Graph pre-training for AMR parsing and generation”.
3. It could be convincing to discuss case studies and error studies to highlight the effectiveness of each proposed component. For example, this paper mentions that the Element-level Graph Pre-training abandons the strategy of capturing the complex structure but focuses directly on the core elements. However, without case study, it is less convincing to figure it out. An example of case study can be found in “Graph pre-training for AMR parsing and generation”.