  Existing PTLM-based models for TSC can be categorized into two groups: 1)
fine-tuning-based models that adopt PTLM as the context encoder; 2)
prompting-based models that transfer the classification task to the text/word
generation task. In this paper, we present a new perspective of leveraging PTLM
for TSC: simultaneously leveraging the merits of both language modeling and
explicit target-context interactions via contextual target attributes.
Specifically, we design the domain- and target-constrained cloze test, which
can leverage the PTLMs' strong language modeling ability to generate the given
target's attributes pertaining to the review context. The attributes contain
the background and property information of the target, which can help to enrich
the semantics of the review context and the target. To exploit the attributes
for tackling TSC, we first construct a heterogeneous information graph by
treating the attributes as nodes and combining them with (1) the syntax graph
automatically produced by the off-the-shelf dependency parser and (2) the
semantics graph of the review context, which is derived from the self-attention
mechanism. Then we propose a heterogeneous information gated graph
convolutional network to model the interactions among the attribute
information, the syntactic information, and the contextual information. The
experimental results on three benchmark datasets demonstrate the superiority of
our model, which achieves new state-of-the-art performance.
