## 3 Methodology
### Overall Architecture  
The general overview structure of DaGATN is shown in Figure 1. The model can be divided into two parts: the construction of a discourse graph and the acquisition of discourse features from this graph structure. The data is pre-processed and fed into a large pre-trained language model as a backbone in the form of \"[CLS]context[SEP]question + answer[SEP]\", obtain token-level feature representation through the encoding layer. The basic discourse units (EDUs), as the basic units in logical reasoning, can be divided into \"explicit\" and \"implicit\" connectives to represent their discourse relations. Explicit relations, i.e., PDTB2.0 [24] contains discourse relations from the manually annotated explicit discourse relations on the 1 million Wall Street Journal (WS) corpus, and implicit relations, i.e., four types of punctuation marks: periods, commas, semicolons, and colons. Details of \"explicit\" and \"implicit\" connectives can be found in Appendix A.  
Clearly, \"explicit\" connectives embody very distinct logical relationships, determined by the intrinsic meanings of the connectives themselves. The discourse in the context,  
Figure 1: Overview Structure of DaGATN.  
such as \"because, if\", is considered to correspond to the logical relations in the text, where \"because\" represents the causal relation and \"if\" refers to the hypothetical relation.  
However, segmenting EDUs solely based on this does not resonate with human linguistic habits. As is widely recognized, humans typically express themselves in units of sentences, and there is a slight pause between sentences. This is intuitively represented in text by a series of punctuation marks such as \"periods\", \"commas\", \"semicolons\", and \"colons\". The logical relationships implied by these punctuation marks are often diverse and hard to define, hence we categorize them as \"implicit\" connectives.  
The context and answers are divided into EDUs using the PDTB2.0 tool, and the Embedding of each EDU relies on its constituent tokens for acquisition. Considering the significant difference between the two connectives, for the pre-processed EDUs, the argument graph and punctuation graph are constructed separately using connectives and punctuation marks as edges. So that when calculating attention coefficients, the two types of edges have independent weight calculation methods. We believe that explicit connectives have a higher priority. Therefore, our strategy is: if both \"punctuation marks\" and \"explicit connectives\" exist between two EDUs, the explicit connective takes precedence.  
Based on the information of the graph, in order to obtain higher-level representations of EDUs to enhance token features and to solve the problem of long-distance dependency between nodes, we used graph attention networks for graph reasoning. Meanwhile, since the graph neural network ignores the anterior-posterior order relationship when reasoning EDUs, we encode the position information of EDU nodes and add it into EDUs Embedding.  
The EDU features will be fused into the embedding according to its corresponding \"constituent token sequence\" after the message passing of the graph attention network, and thus the token features are obtained after the discourse logic enhancement. For the resulting Discourse-enhanced Token Embedding, the sequence of tokens representing the context and the sequence of tokens representing the questions and options are pooled via weighted summation, and then we concatenate them with the \"[CLS]\" embedding output using the backbone model, which represents the global features. Finally, the final answer prediction is performed after mapping by feedforward neural network.