Argumentation mining of Chinese online reviews with pre-training and pointer networks

Abstract

The Internet has generated a large amount of unstructured online texts that contain a large amount of argumentative content. How to automatically extract the argumentative contents from these online texts is an important issue. First, a corpus of argumentation about Chinese online current affairs reviews is built. The existing argumentation annotation scheme is also modified by adding two additional types of elements, "central conclusion" and "intermediate conclusion", and their corresponding argumentation relations to reflect the more complex structure of the long review texts. The pre-trained large models are used to identify and classify the argumentative elements in these texts. Then, the penultimate hidden state of the best model is used as the input of the pointer network for argumentative elements relationship identification. The experimental results display that the "pre-trained large model + pointer network" model improves the performance of the existing models.