  Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims at extracting
aspect terms and opinion terms from review in the form of opinion pairs or
additionally extracting sentiment polarity of aspect term to form opinion
triplet. Because of containing several opinion factors, the complete AFOE task
is usually divided into multiple subtasks and achieved in the pipeline.
However, pipeline approaches easily suffer from error propagation and
inconvenience in real-world scenarios. To this end, we propose a novel tagging
scheme, Grid Tagging Scheme (GTS), to address the AFOE task in an end-to-end
fashion only with one unified grid tagging task. Additionally, we design an
effective inference strategy on GTS to exploit mutual indication between
different opinion factors for more accurate extractions. To validate the
feasibility and compatibility of GTS, we implement three different GTS models
respectively based on CNN, BiLSTM, and BERT, and conduct experiments on the
aspect-oriented opinion pair extraction and opinion triplet extraction
datasets. Extensive experimental results indicate that GTS models outperform
strong baselines significantly and achieve state-of-the-art performance.
