# Robustness Analysis on Graph Neural Networks Model for Event Detection
###### Abstract  
Event Detection (ED), which aims to identify trigger words from the given text and classify them into corresponding event types, is an important task in Natural Language Processing (NLP); it contributes to several downstream tasks and is beneficial for many real-world applications. Most of the current SOTA (state-of-the-art) models for ED are based on Graph Neural Networks (GNN). However, a few studies focus on the issue of GNN-based ED models' robustness towards text adversarial attacks, which is a challenge in practical applications of EDs that needs to be solved urgently. In this paper, we first propose a robustness analysis framework for an ED model. Using this framework, we can evaluate the robustness of the ED model with various adversarial data. To improve the robustness of the GNN-based ED model, we propose a new multi-order distance representation method and an edge representation update method based on attention weights, then design an innovative model named A-MDL-EEGCN. Extensive experiments illustrate that the proposed model can achieve better performance than other models both on original data and various adversarial data. The comprehensive robustness analysis according to experimental results in this paper brings new insights into the evaluation and design of a robust ED model. robustness; graph neural networks; event detection; multi-order distance +
Footnote †: journal: Applied Sciences  
has focused on how to convert a text sequence into graph-structured data to incorporate rich semantic information and then introduce Graph Neural Networks (GNN) into the ED task [18; 19; 20; 21; 22], and the current SOTA (state-of-the-art) models for ED are based on GNN. Most of the above models are based on default and perfect dataset assumption, i.e., the label quality is high, the noise is low, and the distribution is balanced; hence, it is expected that patterns learned from such a dataset can be generalized. However, a real dataset that does not satisfy the completeness tends to result in a trained model that contains the same biases as the training data [23]. The research on ED mainly focuses on the construction of a model and evaluates the model performance according to the metrics on high-quality datasets, such as Precision, Recall, and F1-score, which may lead to an overestimation of the model's ability [24]. In the field of NLP, the robustness of a model is apparently an essential indicator, and a model that has strong adaptability to various input texts is desired for different applications in which their inputs are not always perfect. Although some researchers have begun to pay attention to the robustness of NLP models [25; 26; 27; 28; 29; 30], there are few studies on ED models. The issue of ED models' robustness is a challenge for both theoretical research and the practical application of ED, which needs to be solved urgently. Differently from usual studies that propose a robust model for one specific instance among various kinds of data contamination or imperfections (e.g., modeling the adversarial data or adopting any adversarial optimization objective), our work follows a totally different route toward more general robustness. The main idea is to introduce an endogenous, robust enhancement mechanism rather than propose thousands of over-specific models or methods in isolation. We first propose a Robustness Analysis Framework on an ED Model to evaluate the performance of the ED model facing various text transformations and subpopulations, based on which we can comprehensively analyze the ED model's robustness. To improve the robustness of the GNN-based ED model, we then propose a new multi-order distance representation method (for a syntactic dependency graph of input sentence) to better capture associations between long-distance words and an edge representation update method based on attention weights to better distinguish the importance of different edge types in edge update. The effectiveness of the two methods is verified by extensive experiments.