  Cross-domain Aspect Sentiment Triplet Extraction (ASTE) aims to extract
fine-grained sentiment elements from target domain sentences by leveraging the
knowledge acquired from the source domain. Due to the absence of labeled data
in the target domain, recent studies tend to rely on pre-trained language
models to generate large amounts of synthetic data for training purposes.
However, these approaches entail additional computational costs associated with
the generation process. Different from them, we discover a striking resemblance
between table-filling methods in ASTE and two-stage Object Detection (OD) in
computer vision, which inspires us to revisit the cross-domain ASTE task and
approach it from an OD standpoint. This allows the model to benefit from the OD
extraction paradigm and region-level alignment. Building upon this premise, we
propose a novel method named \textbf{T}able-\textbf{F}illing via \textbf{M}ean
\textbf{T}eacher (TFMT). Specifically, the table-filling methods encode the
sentence into a 2D table to detect word relations, while TFMT treats the table
as a feature map and utilizes a region consistency to enhance the quality of
those generated pseudo labels. Additionally, considering the existence of the
domain gap, a cross-domain consistency based on Maximum Mean Discrepancy is
designed to alleviate domain shift problems. Our method achieves
state-of-the-art performance with minimal parameters and computational costs,
making it a strong baseline for cross-domain ASTE.
