Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction (2020.coling-main)
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| Challenge: | Supervised-learning approaches fail to scale across domains where labeled data is lacking. |
| Approach: | They propose a method for incorporating external linguistic knowledge into a self-attention mechanism coupled with a transformer-based model. |
| Outcome: | The proposed method enables leveraging syntactic knowledge from transformer-based models to bridge the gap between domains. |
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Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification (2020.coling-main)
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| Challenge: | Existing approaches to aspect-level sentiment classification focus on modeling the relationship between aspect words and their contexts with attention, and ignore the use of elaborate knowledge implicit in the context. |
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| Challenge: | Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to identify aspect-sentiment pairs in sentences from a target domain. |
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| Challenge: | Existing approaches to aspect-based sentiment analysis do not fully leverage syntactical information. |
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| Challenge: | Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect Based Sentimence Analysis (ABSA) . recent deep transfer-learning methods have been applied successfully to a myriad of NLP tasks. |
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Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction (P18-1)
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Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis (2020.emnlp-main)
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Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis (2022.findings-acl)
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| Challenge: | Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in a sentence. |
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