  For argumentation mining, there are several sub-tasks such as argumentation
component type classification, relation classification. Existing research tends
to solve such sub-tasks separately, but ignore the close relation between them.
In this paper, we present a joint framework incorporating logical relation
between sub-tasks to improve the performance of argumentation structure
generation. We design an objective function to combine the predictions from
individual models for each sub-task and solve the problem with some constraints
constructed from background knowledge. We evaluate our proposed model on two
public corpora and the experiment results show that our model can outperform
the baseline that uses a separate model significantly for each sub-task. Our
model also shows advantages on component-related sub-tasks compared to a
state-of-the-art joint model based on the evidence graph.
