Summary of Relation-based Counterfactual Data Augmentation and Contrastive Learning For Robustifying Natural Language Inference Models, by Heerin Yang et al.
Relation-based Counterfactual Data Augmentation and Contrastive Learning for Robustifying Natural Language Inference Models
by Heerin Yang, Sseung-won Hwang, Jungmin So
First submitted to arxiv on: 28 Oct 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 proposed method utilizes token-based and sentence-based augmentation techniques to generate counterfactual sentence pairs, which are then used in contrastive learning to help the pre-trained language model learn class semantics. This approach is designed to improve the model’s performance and robustness on natural language inference tasks, particularly when dealing with counterfactually revised data. The method demonstrates improved results on both counterfactually-revised datasets and general NLI benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a way to make pre-trained language models better at understanding natural language by using special techniques to create fake sentence pairs that belong to different categories. This helps the model learn what makes certain sentences belong to one category or another, rather than just relying on patterns it finds in the data. The new method improves how well the model performs and is less affected by counterfactual data. |
Keywords
» Artificial intelligence » Inference » Language model » Semantics » Token
