A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis
This work addresses the problem of accurately predicting sentiment polarity for specific aspects in text, which is important for applications like review analysis, but it appears incremental as it builds on existing encoder-based methods.
The paper tackles aspect-based sentiment analysis by proposing an Aspect-Guided Deep Transition model that uses the given aspect to guide sentence encoding and includes an aspect-oriented objective, resulting in significantly outperforming the best reported results on multiple SemEval datasets.
Aspect based sentiment analysis (ABSA) aims to identify the sentiment polarity towards the given aspect in a sentence, while previous models typically exploit an aspect-independent (weakly associative) encoder for sentence representation generation. In this paper, we propose a novel Aspect-Guided Deep Transition model, named AGDT, which utilizes the given aspect to guide the sentence encoding from scratch with the specially-designed deep transition architecture. Furthermore, an aspect-oriented objective is designed to enforce AGDT to reconstruct the given aspect with the generated sentence representation. In doing so, our AGDT can accurately generate aspect-specific sentence representation, and thus conduct more accurate sentiment predictions. Experimental results on multiple SemEval datasets demonstrate the effectiveness of our proposed approach, which significantly outperforms the best reported results with the same setting.