| dc.description.abstract | For manufacturers of home appliances, the overview and preference of their products on social media is the information that must be collected. The feedback given through the online evaluation can instantly reflect whether the products are acceptable to others, and it can further analyze what information about the product is more discussed. It can make up for the shortcomings and improve their own products in the future.
The method in this paper is divided into two parts. In the first part, we divide the manually labeled home appliance data into 3 subtasks: Named Entity Recognition, Aspect Category Extraction, and Sentiment Classification. In the NER task, we use BERT-BiLSTM-CRF model.In ACE, SC tasks, we refer to Sun et al., adding the information of the auxiliary sentence, and using the BERT classification model. We use the above method to get the basic performance of these 3 tasks. In the second part, in the SC task, we try to train task-oriented models for different aspect category. The goal is to improve the basic performance of SC. In this part, we combine the Reptile algorithm of meta learning and adversarial training concepts to propose an adversarial Reptile algorithm. We hope to combine the advantages of few-shot learning in meta learning and the structure of adversarial training framework to create a model that can be quickly trained on various distributed data.
This research extracts reviews on social media about home appliance and manually labeled them, then cut out training and testing data sets in half. The result shows that in the SC task, without any transfer learning method, the Macro-F1 of training task-oriented models for different aspect category is lower than the benchmark(60.1\% v.s. 68.6\%).After using the adversarial Reptile architecture training model, the Macro-F1 has improved to 70.3\% in task-oriented models. It shows that transfer learning method is helpful for SC task. | en_US |