Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning (D19-1)
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| Challenge: | Existing methods to extract aspects and sentiments are limited due to lack of annotated sequence data. |
| Approach: | They propose a Selective Adversarial Learning method to align latent correlation vectors . they propose tagging a set of aspect boundary tags and sentiment tags to create a joint label space . |
| Outcome: | The proposed method can learn weights for words to achieve fine-grained adaptation. |
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