Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification

Understand

Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT).

• However, due to the especially expensive and labor-intensive labeling, existing public corpora in AT-level are all relatively small.
• Meanwhile, most of the previous methods rely on complicated structures with given scarce data, which largely limits the efficacy of the neural models.
• In this paper, we exploit a new direction named coarse-to-fine task transfer, which aims to leverage knowledge learned from a rich-resource source domain of the coarse-grained AC task, which is more easily accessible, to improve the learning in a low-resource target domain of the fine-grained AT task.