Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis (P19-2)
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| Challenge: | End-to-end deep learning systems lack flexibility as one cannot adjust the network to fix an obvious problem. |
| Approach: | They propose a way to leverage lexicon information to make the model more flexible . they also explore the effect of regularizing attention vectors to allow the network to have a broader "focus" |
| Outcome: | The proposed approach leverages lexicon information to make it more flexible and robust. |
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