  The aspect-based sentiment analysis (ABSA) is a fine-grained task that aims
to determine the sentiment polarity towards targeted aspect terms occurring in
the sentence. The development of the ABSA task is very much hindered by the
lack of annotated data. To tackle this, the prior works have studied the
possibility of utilizing the sentiment analysis (SA) datasets to assist in
training the ABSA model, primarily via pretraining or multi-task learning. In
this article, we follow this line, and for the first time, we manage to apply
the Pseudo-Label (PL) method to merge the two homogeneous tasks. While it seems
straightforward to use generated pseudo labels to handle this case of label
granularity unification for two highly related tasks, we identify its major
challenge in this paper and propose a novel framework, dubbed as
Dual-granularity Pseudo Labeling (DPL). Further, similar to PL, we regard the
DPL as a general framework capable of combining other prior methods in the
literature. Through extensive experiments, DPL has achieved state-of-the-art
performance on standard benchmarks surpassing the prior work significantly.
