End-to-End Joint Opinion Role Labeling with BERT

Abstract

Opinion mining has raised growing interest both in industry and academia in the past decade. Opinion role labeling (ORL) is a task to extract opinion holder and target from natural language to answer the question 'who express what'. Recent years, neural network based methods with additional lexical and syntactic features have achieved state-of-the-art performances in similar tasks. Moreover, Bidirectional Encoder Representations from Transformers (BERT) has shown impressive performances among a variety of natural language processing (NLP) tasks. To investigate BERT based end-to-end model in ORL, we propose models using BERT, Bidirectional Long short-term Memory (BiLSTM) and Conditional Random Field (CRF) to jointly extract opinion roles (e.g., opinion holder and target). Experimental results show that our models achieve remarkable scores without using extra syntactic and/or semantic features. To our best knowledge, we are among the pioneers to successfully integrate BERT in this manner. Our work contributes to the improvement of state-of-the-art aspect-level opinion mining methods and providing strong baselines for future work.