Abstract: Aspect sentiment triplet extraction aims to identify aspect words, opinion words, and corresponding sentiment polarity from sentences. Existing studies have not fully considered the correlation relationship between aspect words and opinion words, and also suffer from the problems of insufficient semantic information extraction and incomplete utilization of background knowledge. To address the above issues, a dual encoder and knowledge enhancement aspect sentiment triplet extraction model is proposed. Firstly, dual encoders of BERT and Bi-LSTM are used simultaneously to mine semantic information in sentences from different levels and integrate external affective knowledge to enhance the sentiment expression of the text. Secondly, the relationship between aspects and opinions is learned interactively and iteratively by position embedding interactive attention. Finally, the triplet is predicted using a boundary-driven table-filling method. The experimental results show that the model can accurately perform triplet extraction by improving the F1 values on the four public datasets by 9.43 percentage points, 7.32 percentage points, 7.43 percentage points and 4.78 percentage points, respectively, compared with the mainstream model GTS-BERT.

Key words: aspect sentiment triplet extraction, dual encoder, interactive attention, affective knowledge, position embedding