Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis (2022)

Abstract: 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.

## introduction 1.aspect-based sentiment analysis the aspect-based sentiment analysis (absa) task aims to recognize the sentiment polarities centered on the considered aspect terms occurring in the sentence. the establishment of the absa task echoes the long-standing literature of conventional sentence-level sentiment analysis (sa). for instance, as shown in figure 1 , a normal absa data annotation tags sentiment score on specific aspect terms in the sentence, like "surroundings" as positive and "food" as negative. meanwhile, in the conventional sentence-based sentiment analysis, the whole sentence is labeled as negative at a coarser granularity. ## dual-granularity pseudo labeling in short, our work focuses on expanding the traditional pl method to utilize coarse-grained datasets. to achieve this goal, we draw inspiration from the multi-task learning community and augment the pl method with a different modeling pathway. consequently, we obtain a framework where two separate pathways are trained synergistically targeted at labels of both granularities. ## conclusion in this paper, we propose dual-granularity pseudo labeling (dpl). dpl extends from the vanilla pseudo-label method and augments it to a dualpathway system. it additionally enforces strong control of information flow directing to the data at different granularities of annotation. the results demonstrate the state-of-the-art performance of dpl on the data-scarce absa task. as a pioneering framework design, we also show that the dpl is compatible with pre-training and multi-task learning methods as published before. in the future, we hope to explore the possibility of dpl in other domains, such as computer vision problems where