  State-of-the-art target-oriented opinion word extraction (TOWE) models
typically use BERT-based text encoders that operate on the word level, along
with graph convolutional networks (GCNs) that incorporate syntactic information
extracted from syntax trees. These methods achieve limited gains with GCNs and
have difficulty using BERT wordpieces. Meanwhile, BERT wordpieces are known to
be effective at representing rare words or words with insufficient context
information. To address this issue, this work trades syntax trees for BERT
wordpieces by entirely removing the GCN component from the methods'
architectures. To enhance TOWE performance, we tackle the issue of aspect
representation loss during encoding. Instead of solely utilizing a sentence as
the input, we use a sentence-aspect pair. Our relatively simple approach
achieves state-of-the-art results on benchmark datasets and should serve as a
strong baseline for further research.
