  Aspect Sentiment Triplet Extraction (ASTE) aims to extract the spans of
aspect, opinion, and their sentiment relations as sentiment triplets. Existing
works usually formulate the span detection as a 1D token tagging problem, and
model the sentiment recognition with a 2D tagging matrix of token pairs.
Moreover, by leveraging the token representation of Pretrained Language
Encoders (PLEs) like BERT, they can achieve better performance. However, they
simply leverage PLEs as feature extractors to build their modules but never
have a deep look at what specific knowledge does PLEs contain. In this paper,
we argue that instead of further designing modules to capture the inductive
bias of ASTE, PLEs themselves contain "enough" features for 1D and 2D tagging:
(1) The token representation contains the contextualized meaning of token
itself, so this level feature carries necessary information for 1D tagging. (2)
The attention matrix of different PLE layers can further capture multi-level
linguistic knowledge existing in token pairs, which benefits 2D tagging. (3)
Furthermore, with simple transformations, these two features can also be easily
converted to the 2D tagging matrix and 1D tagging sequence, respectively. That
will further boost the tagging results. By doing so, PLEs can be natural
tagging frameworks and achieve a new state of the art, which is verified by
extensive experiments and deep analyses.
