Cold-Start Aware User and Product Attention for Sentiment Classification (P18-1)
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| Challenge: | Existing models do not deal with cold-start problem typical in review websites. |
| Approach: | They propose a Hybrid Contextualized Sentiment Classifier that uses word encoder and Cold-Start Aware Attention to pool word vectors. |
| Outcome: | The proposed model performs significantly better on famous datasets despite having less complexity and can be trained much faster. |
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| Challenge: | Typical approaches do not exploit the potential of historical reviews or do not make full use of user/product associations. |
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| Challenge: | Existing work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. |
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Context-aware Embedding for Targeted Aspect-based Sentiment Analysis (P19-1)
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| Challenge: | Existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA. |
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Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis (2021.acl-short)
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| Challenge: | Existing methods for analyzing aspect terms are focused on extracting semantic information inherent within the sentence. |
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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" |
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