Enhancing Argument Structure Extraction with Efficient Leverage of
  Contextual Information
Yun Luo Zhen Yang Fandong Meng Yingjie Li Jie Zhou Yue Zhang
Argument structure extraction (ASE) aims to identify the discourse structure
of arguments within documents. Previous research has demonstrated that
contextual information is crucial for developing an effective ASE model.
However, we observe that merely concatenating sentences in a contextual window
does not fully utilize contextual information and can sometimes lead to
excessive attention on less informative sentences. To tackle this challenge, we
propose an Efficient Context-aware ASE model (ECASE) that fully exploits
contextual information by enhancing modeling capacity and augmenting training
data. Specifically, we introduce a sequence-attention module and
distance-weighted similarity loss to aggregate contextual information and
argumentative information. Additionally, we augment the training data by
randomly masking discourse markers and sentences, which reduces the model's
reliance on specific words or less informative sentences. Our experiments on
five datasets from various domains demonstrate that our model achieves
state-of-the-art performance. Furthermore, ablation studies confirm the
effectiveness of each module in our model.