Enhancing Comparative Opinion Mining with Multi-Dimensional Embeddings

The article discusses the task of Comparative Opinion Mining (COQE), which involves extracting comparative opinions from reviews in Vietnamese. The authors provide a detailed overview of the COQE task, including its formulation and sub-tasks, such as Comparative Sentence Identification (CSI) and Comparative Element Extraction (CEE).
The authors then present their end-to-end architecture for the COQE task, which is based on fine-tuning a pre-trained BERT model. They explain how they use the BERT model to extract hidden representations of reviews and classify them as comparative or non-comparative. The authors also provide results showing that their approach outperforms other competitors in the official private test.
The article concludes by summarizing the conclusions of the paper and discussing potential directions for future research. The authors highlight the importance of demystifying complex concepts by using everyday language and engaging metaphors or analogies to make the content more accessible to a wider audience. They also emphasize the need for further research in this area to improve the accuracy and efficiency of COQE systems.