Improving Low-Resource Chinese Event Detection with Multi-task Learning

Chinese Event Detection (CED) aims to detect events from unstructured sentences. Due to the difficulty of labeling event detection datasets, previous approaches suffer from severe data sparsity problem. To address this issue, we propose a novel Lattice LSTM based multi-task learning model. On one hand, we utilize multi-granularity word information via Lattice LSTM to fully exploit existing datasets. On the other hand, we employ the multi-task learning mechanism to improve CED with datasets from other tasks. Specifically, we combine Name Entity Recognition (NER) and Mask Word Prediction (MWP) as two auxiliary tasks to learn both entity and general language information. Experiments show that our approach outperforms the six SOTA methods by 1.9% on ACE2005 benchmark. The source code is released on https://github.com/tongmeihan1995/MLL-chinese-event-detection.