Unsupervised Data Augmentation for Aspect Based Sentiment Analysis (2022.coling-1)
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| Challenge: | Recent approaches to Aspect-based Sentiment Analysis (ABSA) perform the subtasks of aspect term extraction (ATE) and aspect sentiment classification (ASC) simultaneously. |
| Approach: | They introduce an adaptation of Unsupervised Data Augmentation in semi-supervised learning that performs both aspects of Aspect-based Sentiment Analysis (ABSA) and aspect sentiment classification (ASC) they show that simple augmentations applied to modest-sized datasets along with consistency training lead to competitive performance with current ABSA state-of-the-art in restaurant and laptop domains . |
| Outcome: | The proposed approach performs well on a span-level classification task with minimal training data. |
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) data augmentation has attracted increasing attention in recent years due to data sparsity. |
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| Challenge: | Existing work on Aspect-based sentiment analysis ignores the rich label semantics of ABSA. |
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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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