Cross‐domain sequence labelling using language modelling and parameter generating

Abstract

Sequence labelling (SL) tasks are currently widely studied in the field of natural language processing. Most sequence labelling methods are developed on a large amount of labelled training data via supervised learning, which is time-consuming and expensive. As an alternative, domain adaptation is proposed to train a deep-learning model for sequence labelling in a target domain by exploiting existing labelled training data in related source domains. To this end, the authors propose a Bi-LSTM model to extract more-related knowledge from multi-source domains and learn specific context from the target domain. Further, the language modelling training is also applied to cross-domain adaptability facilitating. The proposed model is extensively evaluated with the named entity recognition and part-of-speech tagging tasks. The empirical results demonstrate the effectiveness of the cross-domain adaption. Our model outperforms the state-of-the-art methods used in both cross-domain tasks and crowd annotation tasks.