Task: Question Answering
Input (English): Experiments ::: Main Results and Analysis ::: Aspect-Category Sentiment Analysis Task Compared with GCAE, our AGDT improves the performance by 2.4% and 1.6% in the “DS” part of the two dataset, respectively. These results demonstrate that our AGDT can sufficiently exploit the given aspect to generate the aspect-guided sentence representation, and thus conduct accurate sentiment prediction. The “HDS”, which is designed to measure whether a model can detect different sentiment polarities in a sentence, consists of replicated sentences with different sentiments towards multiple aspects. Our AGDT surpasses GCAE by a very large margin (+11.4% and +4.9% respectively) on both datasets. Experiments ::: Main Results and Analysis ::: Aspect-Term Sentiment Analysis Task In the “HDS” part, the AGDT model obtains +3.6% higher accuracy than GCAE on the restaurant domain and +4.2% higher accuracy on the laptop domain, which shows that our AGDT has stronger ability for the multi-sentiment problem against GCAE. These results further demonstrate that our model works well across tasks and datasets. 
 Question: How big is the improvement over the state-of-the-art results?
Output (English): AGDT improves the performance by 2.4% and 1.6% in the “DS” part of the two dataset Our AGDT surpasses GCAE by a very large margin (+11.4% and +4.9% respectively) on both datasets In the “HDS” part, the AGDT model obtains +3.6% higher accuracy than GCAE on the restaurant domain and +4.2% higher accuracy on the laptop domain