  Structural bias has recently been exploited for aspect sentiment triplet
extraction (ASTE) and led to improved performance. On the other hand, it is
recognized that explicitly incorporating structural bias would have a negative
impact on efficiency, whereas pretrained language models (PLMs) can already
capture implicit structures. Thus, a natural question arises: Is structural
bias still a necessity in the context of PLMs? To answer the question, we
propose to address the efficiency issues by using an adapter to integrate
structural bias in the PLM and using a cheap-to-compute relative position
structure in place of the syntactic dependency structure. Benchmarking
evaluation is conducted on the SemEval datasets. The results show that our
proposed structural adapter is beneficial to PLMs and achieves state-of-the-art
performance over a range of strong baselines, yet with a light parameter demand
and low latency. Meanwhile, we give rise to the concern that the current
evaluation default with data of small scale is under-confident. Consequently,
we release a large-scale dataset for ASTE. The results on the new dataset hint
that the structural adapter is confidently effective and efficient to a large
scale. Overall, we draw the conclusion that structural bias shall still be a
necessity even with PLMs.
