Papers with discriminative approaches
Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking (2023.acl-long)
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| Challenge: | Existing schema-guided dialogue state tracking models do not account for schema variations and are not generalized to unseen services. |
| Approach: | They propose a new architecture which allows for rich attention among descriptions and history while keeping computation costs constrained. |
| Outcome: | The proposed model outperforms the more than 30x larger D3ST-XXL model on the SGD-X benchmark by 5.0 points. |
Unsupervised Information Extraction: Regularizing Discriminative Approaches with Relation Distribution Losses (P19-1)
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| Challenge: | Existing unsupervised relation extraction models are either generative or discriminative . however, they are hard to train without supervision and are unstable . |
| Approach: | They propose a skewness loss and distribution distance loss to improve the performance of discriminative based models. |
| Outcome: | The proposed models surpass current state-of-the-art on three different datasets. |
Investigating the Working of Text Classifiers (C18-1)
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| Challenge: | Text classification is one of the most widely studied tasks in natural language processing. |
| Approach: | They propose to use large multilayer neural network models to compose meaning of sentences . they propose to disincentivize focusing on key lexicons to improve classification accuracy . |
| Outcome: | The proposed models learn to compose the meaning of the sentences or focus on key lexicons for classifying the document. |
Generative-to-Discriminative Test-Time Adaptation via Manifold-Aware Diffusion and Bayesian Distillation (2026.findings-acl)
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Boyun Zhang, Zequn Xie, Fangming Feng, Zihan Zhang, Yongbo He, Chuxin Wang, Sihang Cai, Tao Jin, Qifei Zhang
| Challenge: | Existing discriminative approaches suffer from "confident but wrong" failure mode, blindly adapting to OOD noise leading to error accumulation. |
| Approach: | They propose a TTA framework that harmonizes the robustness of generative diffusion models with the efficiency of discriminative regression networks via Bayesian Diffusion Distillation (BDD). |
| Outcome: | The proposed framework reduces MAE from 0.6872 to 0.5673 and boosts binary accuracy by 5.81 percentage points (reaching 57.33%) it also reduces the MAE of the MOSI to SIMS shift and achieves an 11.18-point gain over the baseline. |
A Universal Discriminator for Zero-Shot Generalization (2023.acl-long)
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| Challenge: | Generative modeling has been the dominant approach for large-scale pretraining and zeroshot generalization. |
| Approach: | They propose a discriminator that predicts whether a text sample comes from the true data distribution and which option has the highest probability of coming from the real data distribution. |
| Outcome: | The proposed discriminative approach outperforms GANs on a number of NLP tasks by 16.0%, 7.8%, and 11.5% respectively. |
Generative Cross-Modal Retrieval: Memorizing Images in Multimodal Language Models for Retrieval and Beyond (2024.acl-long)
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| Challenge: | Recent advances in generative language models have demonstrated their ability to memorize knowledge from documents and recall knowledge to respond to user queries effectively. |
| Approach: | They propose to enable multimodal large language models to memorize and recall images within their parameters. |
| Outcome: | The proposed model performs well even with large-scale image candidate sets. |