User-in-the-Loop Named Entity Recognition via Counterfactual Learning

Named Entity Recognition (NER) is an important task, to enable a wide range of NLP applications. The state-of-the-art NER models are based on deep learning and require enough labeled data. In practice, the labeled data for NER is usually limited, as providing accurate labels to the sentences is very time consuming. With an NER model trained on limited labeled data, it is desirable to develop an efficient mechanism to collect data labels and improve the model over time. To achieve this, existing works develop active learning approaches. However, these approaches are usually developed for annotators and assume the annotators will provide the exactly correct labels to each sentence selected to label. In this paper, we propose a simple yet effective user-in-the-loop feedback mechanism to enable end users, instead of annotators, to easily provide labels to the system. We identify counterfactual bias of the data collected by this feedback mechanism. To alleviate the bias and achieve more sample-efficient learning, we further develop a counterfactual NER learning framework. We develop an imputation model to estimate the loss in those non-displayed entity classes. By considering both losses on displayed and non-displayed entity classes, we can efficiently alleviate such display bias in the NER model. With extensive experiments, we validate the effectiveness of our feedback mechanism and learning framework.