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State-of-the-art target-oriented opinion word extraction (TOWE) modelstypically use BERT-based text encoders that operate on the word level, alongwith graph convolutional networks (GCNs) that incorporate syntactic informationextracted from syntax trees. These methods achieve limited gains with GCNs andhave difficulty using BERT wordpieces. Meanwhile, BERT wordpieces are known tobe effective at representing rare words or words with insufficient contextinformation. To address this issue, this work trades syntax trees for BERTwordpieces by entirely removing the GCN component from the methods'architectures. To enhance TOWE performance, we tackle the issue of aspectrepresentation loss during encoding. Instead of solely utilizing a sentence asthe input, we use a sentence-aspect pair. Our relatively simple approachachieves state-of-the-art results on benchmark datasets and should serve as astrong baseline for further research.

Trading Syntax Trees for Wordpieces: Target-oriented Opinion Words Extraction with Wordpieces and Aspect Enhancement