Papers with neural text classifier
Privacy-preserving Neural Representations of Text (D18-1)
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| Challenge: | a specific type of attack is used to characterize the privacy of neural representations for NLP tasks, in the context of privacy protection. |
| Approach: | They propose several defense methods based on modified training objectives and characterize the tradeoff between privacy and the utility of neural representations. |
| Outcome: | The proposed defenses improve the privacy of neural representations and characterize the tradeoff between privacy and utility of representations. |
Train, Sort, Explain: Learning to Diagnose Translation Models (N19-4)
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| Challenge: | Evaluating translation models is a trade-off between effort and detail. |
| Approach: | They propose to use a neural text classifier to automatically expose systematic differences between human and machine translations to human experts. |
| Outcome: | The proposed method exposes systematic differences between human and machine translations to human experts. |
LIME: Weakly-Supervised Text Classification without Seeds (2022.coling-1)
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| Challenge: | Existing approaches to weakly-supervised text classification use only label names as sources of supervision. |
| Approach: | They propose a framework for weakly-supervised text classification that replaces seed-word generation with entailment-based pseudo-classification. |
| Outcome: | The proposed framework outperforms baselines and state-of-the-art in 4 benchmarks. |