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Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment Analysis

30 Apr 2018NAACL 2018 6arXiv:1804.11019archive 2025-07-28

Fei Liu, Trevor Cohn, Timothy Baldwin

While neural networks have been shown to achieve impressive results for sentence-level sentiment analysis, targeted aspect-based sentiment analysis (TABSA) --- extraction of fine-grained opinion polarity w.r.t. a pre-defined set of aspects --- remains a difficult task. Motivated by recent advances in memory-augmented models for machine reading, we propose a novel architecture, utilising external "memory chains" with a delayed memory update mechanism to track entities. On a TABSA task, the proposed model demonstrates substantial improvements over state-of-the-art approaches, including those using external knowledge bases.

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Tasks

Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Reading ComprehensionSentenceSentiment Analysis

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Aspect-Based Sentiment Analysis (ABSA) Sentihood Liu et al. Aspect 78.5 #3 of 5 Archive leaderboard report
Aspect-Based Sentiment Analysis (ABSA) Sentihood Liu et al. Sentiment 91.0 #3 of 5 Archive leaderboard report

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