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        <title>Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis</title>
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        <namePart type="given">Fei</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis
%A Liu, Fei
%A Cohn, Trevor
%A Baldwin, Timothy
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F liu-etal-2018-recurrent
%X 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.
%R 10.18653/v1/N18-2045
%U https://aclanthology.org/N18-2045/
%U https://doi.org/10.18653/v1/N18-2045
%P 278-283

Markdown (Informal)

[Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis](https://aclanthology.org/N18-2045/) (Liu et al., NAACL 2018)

• Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis (Liu et al., NAACL 2018)

ACL

• Fei Liu, Trevor Cohn, and Timothy Baldwin. 2018. Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 278–283, New Orleans, Louisiana. Association for Computational Linguistics.