SynSeq4ED: A Novel Event-Aware Text Representation Learning for Event Detection

Event detection (ED) is considered as an important task in natural language processing (NLP) which effectively supports to specify instances of multi event types which are mentioned in text. Recent models adopt advanced neural network architectures, such as long short-term memory (LSTM), graph convolutional network (GCN), etc. to capture the sequential and syntactical representations of texts for leveraging the performance of ED. However, recent neural network-based models neglect to sufficiently perverse both sequential comprehensive meanings as well as syntactical co-referencing relationships between words in the sentences. In this paper, we proposed a novel integration of GCN-based textual syntactical encoder and pre-trained BERT sequential embedding with event-aware masked language mechanism, called SynSeq4ED. In our SynSeq4ED model, we formally present a joint text embedding framework which enable to effectively learn the deep semantic representations of event triggers and arguments by introducing a combination of integrated pre-trained BERT with event-aware masked language strategy and GCN-based syntactical co-referencing text encoding mechanism. The achieved text representations by SynSeq4ED model are then used to improve the performance of multiple tasks in ED, including multiple event detection (MED), few-shot learning event detection (FSLED). Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed SynSeq4ED model in comparing with recent state-of-the-art baselines.