  Aspect-based sentiment analysis (ABSA) aims at analyzing the sentiment of a
given aspect in a sentence. Recently, neural network-based methods have
achieved promising results in existing ABSA datasets. However, these datasets
tend to degenerate to sentence-level sentiment analysis because most sentences
contain only one aspect or multiple aspects with the same sentiment polarity.
To facilitate the research of ABSA, NLPCC 2020 Shared Task 2 releases a new
large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset. In the MAMS dataset,
each sentence contains at least two different aspects with different sentiment
polarities, which makes ABSA more complex and challenging. To address the
challenging dataset, we re-formalize ABSA as a problem of multi-aspect
sentiment analysis, and propose a novel Transformer-based Multi-aspect Modeling
scheme (TMM), which can capture potential relations between multiple aspects
and simultaneously detect the sentiment of all aspects in a sentence.
Experiment results on the MAMS dataset show that our method achieves noticeable
improvements compared with strong baselines such as BERT and RoBERTa, and
finally ranks the 2nd in NLPCC 2020 Shared Task 2 Evaluation.
