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The aspect-based sentiment analysis (ABSA) is a fine-grained task that aimsto determine the sentiment polarity towards targeted aspect terms occurring inthe sentence. The development of the ABSA task is very much hindered by thelack of annotated data. To tackle this, the prior works have studied thepossibility of utilizing the sentiment analysis (SA) datasets to assist intraining the ABSA model, primarily via pretraining or multi-task learning. Inthis article, we follow this line, and for the first time, we manage to applythe Pseudo-Label (PL) method to merge the two homogeneous tasks. While it seemsstraightforward to use generated pseudo labels to handle this case of labelgranularity unification for two highly related tasks, we identify its majorchallenge in this paper and propose a novel framework, dubbed asDual-granularity Pseudo Labeling (DPL). Further, similar to PL, we regard theDPL as a general framework capable of combining other prior methods in theliterature. Through extensive experiments, DPL has achieved state-of-the-artperformance on standard benchmarks surpassing the prior work significantly.

Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis