@InProceedings{liu-cohn-baldwin:2018:N18-2,
  author    = {Liu, Fei  and  Cohn, Trevor  and  Baldwin, Timothy},
  title     = {Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {278--283},
  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.},
  url       = {http://www.aclweb.org/anthology/N18-2045}
}