Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification
Yi Hong Tay, Anh Tuan Luu, Siu Cheung Hui, Jian Su · 2018
This paper proposes a new neural architecture that exploits readily available sentiment lexicon resources.The key idea is that that incorporating a word-level prior can aid in the representation learning process, eventually improving model performance.To this end, our model employs two distinctly unique components, i.e., (1) we introduce a lexicon-driven contextual attention mechanism to imbue lexicon words with long-range contextual information and (2), we introduce a contrastive co-attention mechanism that models contrasting polarities between all positive and negative words in a sentence.Via extensive experiments, we show that our approach outperforms many other neural baselines on sentiment classification tasks on multiple benchmark datasets.