Summary of Adaptive Data Augmentation For Aspect Sentiment Quad Prediction, by Wenyuan Zhang et al.
Adaptive Data Augmentation for Aspect Sentiment Quad Prediction
by Wenyuan Zhang, Xinghua Zhang, Shiyao Cui, Kun Huang, Xuebin Wang, Tingwen Liu
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Aspect sentiment quad prediction (ASQP) is a crucial task in aspect-based sentiment analysis, aiming to predict quad sentiment elements from given sentences. However, the data imbalance issue has been neglected in this field. This paper addresses this problem by dividing it into two aspects: quad-pattern imbalance and aspect-category imbalance. The proposed Adaptive Data Augmentation (ADA) framework tackles the imbalance by adaptively enhancing tail quad patterns and aspect categories through a condition function. Additionally, the study explores a generative framework for extracting complete quads using category prior knowledge and syntax-guided decoding targets. Experimental results show that data augmentation can improve performance, with the ADA method outperforming naive oversampling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ASQP is an important task in understanding how people feel about specific aspects of a product or service. The problem is that there’s not enough training data for this task. This paper tries to solve this issue by making the available data better match real-life situations. It does this by “augmenting” the data, which means creating new examples based on what we already have. The researchers also look at how to generate complete quads (sets of sentiment elements) using prior knowledge and rules about language. By doing these two things, they show that their method improves performance in predicting quad sentiment elements. |
Keywords
» Artificial intelligence » Data augmentation » Syntax
