  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.
