Papers with NCE
Self-Normalization Properties of Language Modeling (C18-1)
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| Challenge: | Existing methods to reduce run-times for language models with large word vocabularies are based on noise contrastive estimation (NCE) |
| Approach: | They propose to use noise-constrained noise-based models to approximate the normalized probability of a class without having to compute the partition function. |
| Outcome: | The proposed model outperforms softmax-based models in a variety of NLP tasks and is based on the noise-constrained noise-constant estimation properties. |
Adversarial Contrastive Estimation (P18-1)
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| Challenge: | Noise contrastive estimation (NCE) is a general strategy used in word embeddings and translations for knowledge graphs. |
| Approach: | They propose to augment negative sampler into mixture distribution with adversarially learned sampler and to combine it with noise contrastive estimation (NCE) they observe faster convergence and improved results on multiple metrics. |
| Outcome: | The proposed model performs better on word embeddings, order embedds and knowledge graph embeddments and faster convergence and improved results on multiple metrics. |
Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models (2021.eacl-main)
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| Challenge: | Existing calibration methods rescale posterior distributions of classifiers after training. |
| Approach: | They propose to use a noise contrastive estimation technique to train an energy-based model during finetuning of pretrained text encoders. |
| Outcome: | The proposed model can reach a better calibration competitive to strong baselines with little or no loss in accuracy. |
Learning with Noise-Contrastive Estimation: Easing training by learning to scale (C18-1)
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| Challenge: | Neural language models have recently shown great improvement, but they share a common issue: large output vocabulary, computational time, and high dimensional space. |
| Approach: | They propose to make scaling factor a trainable parameter and use noise distribution to initialize output bias. |
| Outcome: | The proposed training strategies yield stable and competitive performances in small and large scale language modelling tasks. |
Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency (D18-1)
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| Challenge: | Conditional models are frequently encountered in practice, but there has not been a rigorous theoretical analysis of NCE in this setting. |
| Approach: | They propose to use a ranking-based and ranking-only method for conditional models to estimate parameter estimates. |
| Outcome: | The proposed method avoids calculation of partition function or derivatives at each training step . it is closely related to negative sampling methods, now widely used in NLP . |
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs (2023.emnlp-main)
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| Challenge: | Recent studies on relation representation learning focus on contrastive learning strategies, but these studies overlook important aspects. |
| Approach: | They propose to use within-sentence pairs augmentation and cross-sentent pairs extraction to increase diversity of positive pairs and strengthen the discriminative power of contrastive learning. |
| Outcome: | The proposed task increases diversity of positive pairs and strengthens discriminative power . it overcomes limitations of traditional Relation Extraction tasks, which require manual annotations . |