  Information Extraction (IE) aims to extract structured information from
heterogeneous sources. IE from natural language texts include sub-tasks such as
Named Entity Recognition (NER), Relation Extraction (RE), and Event Extraction
(EE). Most IE systems require comprehensive understandings of sentence
structure, implied semantics, and domain knowledge to perform well; thus, IE
tasks always need adequate external resources and annotations. However, it
takes time and effort to obtain more human annotations. Low-Resource
Information Extraction (LRIE) strives to use unsupervised data, reducing the
required resources and human annotation. In practice, existing systems either
utilize self-training schemes to generate pseudo labels that will cause the
gradual drift problem, or leverage consistency regularization methods which
inevitably possess confirmation bias. To alleviate confirmation bias due to the
lack of feedback loops in existing LRIE learning paradigms, we develop a
Gradient Imitation Reinforcement Learning (GIRL) method to encourage
pseudo-labeled data to imitate the gradient descent direction on labeled data,
which can force pseudo-labeled data to achieve better optimization capabilities
similar to labeled data. Based on how well the pseudo-labeled data imitates the
instructive gradient descent direction obtained from labeled data, we design a
reward to quantify the imitation process and bootstrap the optimization
capability of pseudo-labeled data through trial and error. In addition to
learning paradigms, GIRL is not limited to specific sub-tasks, and we leverage
GIRL to solve all IE sub-tasks (named entity recognition, relation extraction,
and event extraction) in low-resource settings (semi-supervised IE and few-shot
IE).
