Identifying event instance in text plays a critical role in building

NLP applications such as Information Extraction (IE)

system. However, most existing methods for this task focus

only on monolingual clues of a specific language and ignore

the massive information provided by other languages.

Data scarcity and monolingual ambiguity hinder the performance

of these monolingual approaches. In this paper, we

propose a novel multilingual approach — dubbed as Gated

MultiLingual Attention (GMLATT) framework — to address

the two issues simultaneously. In specific, to alleviate

data scarcity problem, we exploit the consistent information

in multilingual data via context attention mechanism.

Which takes advantage of the consistent evidence in multilingual

data other than learning only from monolingual data. To

deal with monolingual ambiguity problem, we propose gated

cross-lingual attention to exploit the complement information

conveyed by multilingual data, which is helpful for the disambiguation.

The cross-lingual attention gate serves as a sentinel

modelling the confidence of the clues provided by other

languages and controls the information integration of various

languages. We have conducted extensive experiments on

the ACE 2005 benchmark. Experimental results show that our

approach significantly outperforms state-of-the-art methods.