Latent-Variable Generative Models for Data-Efficient Text Classification (D19-1)
Copied to clipboard
| Challenge: | Generative classifiers offer potential advantages over discriminative classifications, including data efficiency and zero-shot learning. |
| Approach: | They introduce discrete latent variables into generative story to improve classifiers' performance . they empirically characterize performance of their models on six text classification datasets . |
| Outcome: | The proposed model outperforms discriminative and generative classifiers on six text classification datasets. |
Similar Papers
Discrete Latent Variable Representations for Low-Resource Text Classification (2020.acl-main)
Copied to clipboard
| Challenge: | Several approaches to learning discrete latent variable models for text are available. |
| Approach: | They compare several approaches to learning discrete latent variable models for text in the case where exact marginalization over these variables is intractable. |
| Outcome: | The learned models outperform the previous best models in low-resource settings while learning significantly more compressed representations. |
Generative or Discriminative? Revisiting Text Classification in the Era of Transformers (2025.emnlp-main)
Copied to clipboard
Siva Rajesh Kasa, Karan Gupta, Sumegh Roychowdhury, Ashutosh Kumar, Yaswanth Biruduraju, Santhosh Kumar Kasa, Pattisapu Nikhil Priyatam, Arindam Bhattacharya, Shailendra Agarwal, Vijay Huddar
| Challenge: | generative classifiers exhibit lower sample complexity but higher asymptotic error in simple linear settings, a trade-off that remains unexplored in the transformer era. |
| Approach: | They propose to evaluate generative and discriminative architectures for text classification using a generative model that learns the conditional probability distribution P (y|x) generative models are known to work better in low-data settings, giving rise to the classical 'two regimes' phenomenon for classification. |
| Outcome: | The proposed models show that the classical 'two regimes' manifests distinctly across different architectures and training paradigms. |
Deep Latent Variable Models of Natural Language (D18-3)
Copied to clipboard
| Challenge: | In this tutorial, we will discuss the challenges of applying neural variational inference to NLP problems. |
| Approach: | The tutorial will cover deep latent variable models in the case where exact inference over the latent variables is tractable. |
| Outcome: | The proposed tutorial will cover deep latent variable models in the case where inference cannot be performed tractably and when it is not . |
Generative Text Modeling through Short Run Inference (2021.eacl-main)
Copied to clipboard
| Challenge: | Latent variable models for text capture global semantic and syntactic features when trained correctly. |
| Approach: | They propose a short run dynamics for inference that initializes from the prior distribution of the latent variable and runs a small number of Langevin dynamics steps guided by its posterior distribution. |
| Outcome: | The proposed model is able to generate coherent sentences with smooth transition and shows no sign of posterior collapse. |
Better Exploiting Latent Variables in Text Modeling (P19-1)
Copied to clipboard
| Challenge: | Consistent gains in performance on two datasets, Penn Treebank and Yahoo, indicate the generalizability of our method. |
| Approach: | They propose a method to exploit latent variables through hidden state averaging by sampling latent variable multiple times at a gradient step. |
| Outcome: | The proposed method shows consistent gains on two datasets showing that it is generalizable. |
Latent Structure Models for Natural Language Processing (P19-4)
Copied to clipboard
| Challenge: | Latent structure models are a powerful tool for compositional data modeling and pipelines. |
| Approach: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
| Outcome: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Disentangled representation learning aims to provide an interpretable representation of latent features and a framework for controlling the change of specific features. |
| Approach: | They propose a Variational Autoencoder based method which models language features as discrete variables and encourages independence between variables for learning disentangled representations. |
| Outcome: | The proposed model outperforms baselines on several qualitative and quantitative benchmarks and on a text style transfer downstream application. |
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation (N19-1)
Copied to clipboard
| Challenge: | Text generation with generative adversarial networks (GANs) can be divided into text-based and code-based categories depending on the type of signals used for discrimination. |
| Approach: | They propose a text-based approach to exploit generative adversarial networks (GANs) by using autoencoders to provide a continuous representation of sentences, which they will refer to as soft-text, and hybrid latent code and text-oriented approaches with one or more discriminators. |
| Outcome: | The proposed approach outperforms the traditional GAN-based methods on two well-known datasets. |
Making Use of Latent Space in Language GANs for Generating Diverse Text without Pre-training (2021.eacl-srw)
Copied to clipboard
| Challenge: | Existing models for generating diverse texts are not pre-trained . generative adversarial networks suffer from mode-collapsing if they are not trained . |
| Approach: | They propose a GAN model that produces diverse texts conditioned by latent code . they propose to use Gumbel-Softmax distribution for word sampling . |
| Outcome: | The proposed model is competitive with existing models, which requires pre-training. |
Empirical Prior for Text Autoencoders (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Variational Autoencoders (VAE) are used to train generative models with latent variables. |
| Approach: | They propose a transition from Variational Autoencoders (VAE) to text autoencodeurs (AE) which model a compact latent space and preserves the capability of the language model itself. |
| Outcome: | The proposed method generates higher quality and more diverse text than the VAE-based Transformer baselines, and is more efficient than previous approaches. |