A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis

September 01, 2019 · Entered Twilight · 🏛 Conference on Empirical Methods in Natural Language Processing

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Repo contents: README.md, data, run_train_14.sh, run_train_l.sh, run_train_large.sh, run_train_r.sh, thumt

Authors Yunlong Liang, Fandong Meng, Jinchao Zhang, Jinan Xu, Yufeng Chen, Jie Zhou arXiv ID 1909.00324 Category cs.CL: Computation & Language Citations 48 Venue Conference on Empirical Methods in Natural Language Processing Repository https://github.com/XL2248/AGDT ⭐ 21 Last Checked 1 month ago

Abstract

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.