Aspect Sentiment Triplet Extraction: A Seq2Seq Approach With Span Copy Enhanced Dual Decoder

Aspect Sentiment Triplet Extraction (ASTE) is a relatively new and very challenging task that attempts to provide an integral solution for aspect-based sentiment analysis. Aspect sentiment triplets in a sentence usually have overlaps when, e.g., one aspect is associated with multiple opinions and vice versa. Recently, end-to-end ASTE methods are becoming more and more popular for they can avoid the error propagation problem of pipeline-based methods. However, existing tagging-based end-to-end methods face difficulty to obtain a satisfactory recall, and generative methods fail to take a full account of the underlying interactions between aspects, opinions and their corresponding sentiments. In this paper, we formalize the ASTE task as a Seq2Seq learning problem with span copy mechanism for extracting multiple and possibly overlapped triplets. A novel dual decoder is devised purposefully for the ASTE task, where a multi-head attention based span copy mechanism is proposed to copy multi-token aspects and opinions. The dual decoder benefits from the rich output of encoder that can fuse multi-type information including word semantic, POS tag and BIO tag. Experiments on various benchmark datasets demonstrate that our approach achieves new state-of-the-art results. We also conduct analytical experiments to verify the effectiveness of various model components particularly for overlapped triplets extraction. We find that our model can be further improved through data augmentation and post-training.