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. |
Similar Papers
Learning Disentangled Representations for Natural Language Definitions (2023.findings-eacl)
Copied to clipboard
| Challenge: | Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing. |
| Approach: | They propose to use syntactic and semantic regularities in textual data to provide models with both structural biases and generative factors. |
| Outcome: | The proposed model outperforms baselines on several qualitative and quantitative benchmarks and improves the results in the downstream task of definition modeling. |
Learning Disentangled Representations of Negation and Uncertainty (2022.acl-long)
Copied to clipboard
| Challenge: | Negation and uncertainty modeling are long-standing tasks in natural language processing. |
| Approach: | They propose to disentangle negation, uncertainty, and content using a Variational Autoencoder by supervising latent representations using auxiliary objectives. |
| Outcome: | The proposed model can disentangle negation, uncertainty, and content using a Variational Autoencoder. |
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. |
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 . |
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for task-specific natural language generation do not contain any labeled examples. |
| Approach: | They propose a variational autoencoder with disentanglement priors for task-specific natural language generation with none or a handful of task-related labeled examples. |
| Outcome: | The proposed model outperforms baseline models in terms of data augmentation and text style transfer in the few-shot setting. |
Variational Inference and Deep Generative Models (P18-5)
Copied to clipboard
| Challenge: | Unsupervised and semi-supervised learning has been addressed scarcely in NLP . this tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
| Approach: | This tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
| Outcome: | This tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
A Stochastic Decoder for Neural Machine Translation (P18-1)
Copied to clipboard
| Challenge: | Neural machine translation models do not account for local lexical and syntactic variation in parallel corpora. |
| Approach: | They propose a deep generative model of machine translation which incorporates a chain of latent variables to account for local lexical and syntactic variation in parallel corpora. |
| Outcome: | The proposed model consistently improves over strong baselines on several different language pairs. |
An Evaluation of Disentangled Representation Learning for Texts (2021.findings-acl)
Copied to clipboard
| Challenge: | Disentangled representations of texts encode information pertaining to different aspects of the text in separate vector embeddings. |
| Approach: | They propose to use a highly-structured natural language dataset to evaluate disentangled representations for texts. |
| Outcome: | The proposed models are well-suited for learning disentangled representations of texts on a synthetic natural language dataset. |
Attribute Alignment: Controlling Text Generation from Pre-trained Language Models (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models can generate text with sentiment polarity or specific topics without changing the original model parameters. |
| Approach: | They propose a method for controlling text generation by aligning disentangled attribute representations. |
| Outcome: | The proposed method shows large performance gains while maintaining diversity and fluency. |
Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for learning disentangled representations of real-world data focus on attribute labels or unsupervised methods that manipulate factorization in the latent space of models such as the variational autoencoder (VAE). |
| Approach: | They propose an approach that disentangles select attributes in the latent space based on proximity measures reflecting the similarity between data points with respect to these attributes. |
| Outcome: | The proposed method outperforms the VAE baseline and is competitive with state-of-the-art approaches while being more a general framework applicable to other attribute disentanglement tasks. |