Summary of Multi-aspect Controllable Text Generation with Disentangled Counterfactual Augmentation, by Yi Liu and Xiangyu Liu and Xiangrong Zhu and Wei Hu
Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation
by Yi Liu, Xiangyu Liu, Xiangrong Zhu, Wei Hu
First submitted to arxiv on: 30 May 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 The paper proposes MAGIC, a novel multi-aspect controllable text generation method that tackles the issue of imbalanced attribute correlations in training samples. Existing works neglect these correlations, leading to biased models. MAGIC uses disentangled counterfactual augmentation to alleviate this problem during training and target-guided augmentation during inference. The approach outperforms state-of-the-art baselines in both imbalanced and balanced scenarios, demonstrating the effectiveness of MAGIC in controlling text generation attributes from multiple aspects (e.g., sentiment and topic). The proposed method can be applied to various natural language processing tasks that require controlled text generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want a computer to generate text that meets certain conditions, like being positive or talking about sports. Currently, these machines have trouble controlling the text because they don’t understand how different attributes are connected. This paper proposes a new method called MAGIC that helps computers generate better text by recognizing and using these connections. By doing so, MAGIC can create more accurate and controlled text generation results. The researchers tested MAGIC and found it outperformed other methods in various scenarios. |
Keywords
» Artificial intelligence » Inference » Natural language processing » Text generation
