Implicit event argument extraction (EAE) is a crucial document-level information extraction task that aims to identify event arguments beyond the sentence level. Despite many efforts for this task, the lack of enough training data has long impeded th
e study. In this paper, we take a new perspective to address the data sparsity issue faced by implicit EAE, by bridging the task with machine reading comprehension (MRC). Particularly, we devise two data augmentation regimes via MRC, including: 1) implicit knowledge transfer, which enables knowledge transfer from other tasks, by building a unified training framework in the MRC formulation, and 2) explicit data augmentation, which can explicitly generate new training examples, by treating MRC models as an annotator. The extensive experiments have justified the effectiveness of our approach --- it not only obtains state-of-the-art performance on two benchmarks, but also demonstrates superior results in a data-low scenario.
We study the problem of Cross-lingual Event Argument Extraction (CEAE). The task aims to predict argument roles of entity mentions for events in text, whose language is different from the language that a predictive model has been trained on. Previous
work on CEAE has shown the cross-lingual benefits of universal dependency trees in capturing shared syntactic structures of sentences across languages. In particular, this work exploits the existence of the syntactic connections between the words in the dependency trees as the anchor knowledge to transfer the representation learning across languages for CEAE models (i.e., via graph convolutional neural networks -- GCNs). In this paper, we introduce two novel sources of language-independent information for CEAE models based on the semantic similarity and the universal dependency relations of the word pairs in different languages. We propose to use the two sources of information to produce shared sentence structures to bridge the gap between languages and improve the cross-lingual performance of the CEAE models. Extensive experiments are conducted with Arabic, Chinese, and English to demonstrate the effectiveness of the proposed method for CEAE.
