Aspect Sentiment Triplet Extraction (ASTE) is the task to extract aspects, opinions and associated sentiments from sentences. Previous studies do not adequately consider the complicated interactions between aspect and opinion terms in both extraction logic and strategy. We present a novel Double Policy Network with Multi-Tag based Reward model (DPN-MTR), which adopts two networks ATE, TSOTE and a Trigger Mechanism to execute ASTE task following a more logical framework. A Multi-Tag based reward is also proposed to solve the limitations of existing studies for identifying aspect/opinion terms with multiple tokens (one term may consist of two or more tokens) to a certain extent. Extensive experiments are conducted on four widely-used benchmark datasets, and demonstrate the effectiveness of our model in generally improving the performance on ASTE significantly.

In human's expression recognition, the representation of expression features is essential for the recognition accuracy. In this work we propose a novel approach for extracting expression dynamic features from facial expression videos. Rather than utilising statistical models e.g. Hidden Markov Model (HMM), our approach integrates expression dynamic features into a static image, the Histogram Variances Face (HVF), by fusing histogram variances among the frames in a video. The HVFs can be automatically obtained from videos with different frame rates and immune to illumination interference. In our experiments, for the videos picturing the same facial expression, e.g., surprise, happy and sadness etc., their corresponding HVFs are similar, even though the performers and frame rates are different. Therefore the static facial recognition approaches can be utilised for the dynamic expression recognition. We have applied this approach on the well-known Cohn-Kanade AU-Coded Facial Expression database then classified HVFs using PCA and Support Vector Machine (SVMs), and found the accuracy of HVFs classification is very encouraging.