GGNN @Causal News Corpus 2022:Gated Graph Neural Networks for Causal Event Classification from Social-Political News Articles (2022)

Abstract: The discovery of causality mentions from text is a core cognitive concept and appears in many natural language processing (NLP) applications. In this paper, we study the task of Event Causality Identification (ECI) from social-political news. The aim of the task is to detect causal relationships between event mention pairs in text. Although deep learning models have recently achieved a state-of-the-art performance on many tasks and applications in NLP, most of them still fail to capture rich semantic and syntactic structures within sentences which is key for causality classification. We present a solution for causal event detection from social-political news that captures semantic and syntactic information based on gated graph neural networks (GGNN) and contextualized language embeddings. Experimental results show that our proposed method outperforms the baseline model (BERT (Bidirectional Embeddings from Transformers) in terms of f1-score and accuracy.

## introduction causality is a core cognitive concept and appears in many natural language processing (nlp) tasks. we can define causality in generic terms as a semantic relationship between two arguments known as cause and effect. the occurrence of one argument (cause argument) causes the occurrence of the other (effect argument) @xcite . event causality identification (eci) is a task that identifies causal relationships between events from a given text @xcite . to understand how documents containing causal relationships are identified, we present a sample of 5 sentences highlighting causes, effects and causal-markers leading to the rationale for classifying different documents in figure 1 . let us take an example of two sentences; sentence 1: "the protests spread to 15 other towns and resulted in two death and the destruc-tion of property" and sentence 5: "the properties including houses, banks were destroyed" as shown in figure 1 . sentence 1 is causal and sentence 5 is non-causal. the first sentence is regraded as causal because it has the cause (in blue color) and effect (in green color) linked by a causal-marker (in red color) unlike the 5-th sentence which only has the effect. in general, an expression is regarded as noncausal if any of the following conditions are satisfied; (1) the reader is unable to construct a "why" question regarding the effect, (2) the cause does not precede the effect in time, (3) the effect is equally likely to occur or not without the cause and (4) the cause and effect can be swapped without change in meaning @xcite . event causality identification has been actively studied in information retrieval with deep learning as the dominant approach delivering state-of-theart performance @xcite @xcite . bert @xcite has been utilized for automatic event causality detection on the causal news corpus (a dataset used in this study) @xcite . the challenge with deep learning models is that they represent documents as a sequence of tokens either using the traditional count based methods or embedding based methods yet the task of causality detection requires understanding rich structures and reasoning within a sentence. the main contribution of this work is the use of the gated graph neural networks (ggnn) initialized with contextualized language representations on the task of causal event detection from social-political news. ## related work and background in this section, we highlight some of the related work and background information relevant to our proposed methodology. ## methodology in this section, we describe our proposed methodology for the task of event causality identification from social-political news. ## conclusion in this work, we propose a novel deep learning approach for event causality detection from socialpolitical news articles. our proposed approach use gated graph neural networks and contextualized language representations which represent text documents as a graph and model complex semantic relationships ideal for causality detection. experimental results reveal that our proposed model improves performance over the baseline comparison model (bert) in terms of accuracy (80.06% versus 77.81%) and f 1-score (82.58% versus 81.12%).