A Transformer-based Approach for Identifying Target-oriented Opinions from Travel Reviews

Performing target-oriented opinion word extraction (TOWE) from online travel reviews is a valuable reference for both tourists and attraction administration department. This paper formulates a novel research topic of identifying target-opinion pair from Chinese travel review corpus. Learning target-oriented representation accurately, locating the opinion word and extracting the complete opinion are three major challenges. Hence, we leverage aspect-based query, pos-tag and relative position and devise appropriate structure to fuse them in an encoder-decoder framework. Specifically, in the encoder, the target-fused (aspect, review) pair and the pos-tag label are encoded by transformers to model the global dependency, in the decoder, a BiLSTM is adopted to enhance contextual representation by incorporating relative position information. A real-world Chinese travel dataset for TOWE task is constructed, and the experimental results demonstrate the efficacy of the proposed model. Extensive ablation experiments are also conducted to study the effect of different components of the model.