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

arXiv: https://arxiv.org/abs/1804.11019 · HTML: https://mlsanity.com/paper/1804.11019/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [Exploring Conditional Text Generation for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2110.02334/) (2021)
- [BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis](https://mlsanity.com/paper/2203.11702/) (2022)

## Abstract (arXiv)

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.