Papers with VAEs
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders (2022.findings-acl)
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| Challenge: | Variational autoencoders (VAEs) have been widely applied in text generation tasks, but they suffer from insufficient representation capacity and poor controllability. |
| Approach: | They propose a data-driven prior that has expressivity and controllability. |
| Outcome: | The proposed prior enjoys expressivity and controllability and can be used in language modeling and controlled text generation. |
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation (D19-56)
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| Challenge: | Variational Autoencoders suffer from learning uninformative latent representations due to issues such as approximated posterior collapse or entanglement of the latent space. |
| Approach: | They propose to impose an explicit constraint on the Kullback-Leibler divergence term inside the VAE objective function to understand the significance of the KL term in controlling the information transmitted through the VAe channel. |
| Outcome: | The proposed constraint avoids posterior collapse, but it also controls the information transmitted through the VAE channel. |
Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders (2024.findings-naacl)
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| Challenge: | Existing studies on syntactic injection in Variational AutoEncoders (VAEs) are limited to LSTM-based VAEs. |
| Approach: | They propose to use latent space separation techniques to inject syntactic information into Variational AutoEncoders (VAEs) using graph-based models. |
| Outcome: | The proposed end-to-end VAE architecture can improve the organisation of the latent space, alleviating the information loss occurring in standard VAE setups, and resulting in enhanced performances on language modelling and downstream generation tasks. |
APo-VAE: Text Generation in Hyperbolic Space (2021.naacl-main)
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| Challenge: | Existing models that learn embeddings only in Euclidean vector space do not account for such structural property of language. |
| Approach: | They propose a Poincare Variational Autoencoder to capture latent hierarchies in hyperbolic space . they propose enabling adversarial learning procedures to empower robust model training . |
| Outcome: | The proposed model outperforms existing models in a hyperbolic latent space . it captures latent language hierarchies in hyperbolical space and is robust to training . |
Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution (2020.coling-main)
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| Challenge: | Existing Variational autoencoders are limited by the assumed Gaussianity of the underlying probability distributions in the latent space. |
| Approach: | They propose a probabilistic autoencoding framework to deal with a supervised authorship attribution task. |
| Outcome: | The proposed method outperforms existing methods on an Amazon review dataset. |
LangVAE and LangSpace: Building and Probing for Language Model VAEs (2025.emnlp-demos)
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| Challenge: | a novel framework for modular construction of variational autoencoders (VAEs) on pre-trained large language models (LLMs) is presented. |
| Approach: | They propose a modular framework for variational autoencoders on top of pre-trained large language models. |
| Outcome: | The proposed framework can encode pre-trained language models into more compact and semantically disentangled representations. |
On the Effect of Isotropy on VAE Representations of Text (2022.acl-short)
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| Challenge: | Injecting desired geometric properties into text representations has attracted a lot of attention due to its better utilisation of representation space. |
| Approach: | They propose to use an isotropic Gaussian posterior instead of the ellipsoidal Gausssian priori to inject isotropy into text representations. |
| Outcome: | The proposed method improves classification performance, robustness to input perturbation, and generative behavior compared to the ellipsoidal Gaussian posterior. |
Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders (2024.findings-eacl)
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| Challenge: | Recent work has struggled to achieve consistent results due to the inevitable loss of semantic information in the variational bottleneck and limited control over the decoding mechanism. |
| Approach: | They propose a model that leverages the controllability of VQVAE to guide the self-attention mechanism in Transformer-based VAEs to improve semantic control and generation. |
| Outcome: | The proposed model outperforms existing state-of-the-art VAE models in terms of control and preservation of semantic information across different tasks. |
Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation (P18-1)
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| Challenge: | Existing encoder-decoder dialog models cannot output interpretable actions as in traditional systems. |
| Approach: | They propose an unsupervised discrete sentence representation learning method that integrates with existing encoder-decoder dialog models for interpretable response generation. |
| Outcome: | The proposed model can be integrated with existing encoder-decoder dialog models and discover interpretable semantics via either auto encoding or context predicting. |
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase (2021.eacl-main)
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| Challenge: | a data augmentation technique is used to boost performance on spoken language understanding tasks. |
| Approach: | They propose a data augmentation technique based on byte pair encoding and a BERT-like self-attention model to boost performance on spoken language understanding tasks. |
| Outcome: | The proposed method performs well on domain and intent classification tasks for a voice assistant and in a user-study focused on utterance naturalness and semantic similarity. |
Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models (P19-1)
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| Challenge: | Variational autoencoders (VAEs) have received much attention as an end-to-end architecture for text generation with latent variables. |
| Approach: | They propose to leverage several multi-level structures to learn a variational autoencoder model for generating long, and coherent text. |
| Outcome: | The proposed model produces more coherent and less repetitive long text compared to baselines and mitigates posterior collapse issue. |
Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders (D19-1)
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| Challenge: | Existing work on variableal autoencoders and waterstein autoencoding models has shown significant progress in open-domain response generation. |
| Approach: | They propose to embed user-level and utterance-level information into two multimodal distributions and combine them into a mixed distribution. |
| Outcome: | The proposed model outperforms state-of-the-art models on a large-scale real-world dataset. |
A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews (2021.naacl-main)
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| Challenge: | Existing topic models may extract topics associated with writers’ subjective opinions mixed with those related to factual descriptions. |
| Approach: | They propose a neural topic model combined with adversarial training to disentangle opinion topics from plot and neutral ones. |
| Outcome: | The proposed model shows improved coherence and variety of topics, consistent disentanglement rate, and superior sentiment classification performance to other supervised topic models. |
FlowPrior: Learning Expressive Priors for Latent Variable Sentence Models (2021.naacl-main)
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| Challenge: | Existing training strategies are not effective for learning rich priors, we propose adding the importance-sampled log marginal likelihood as a second term to the standard VAE objective. |
| Approach: | They propose to add importance-sampled log marginal likelihood to standard VAE objective to help when learning the prior. |
| Outcome: | The proposed model improves language modeling tasks compared to baselines. |
Learning Robust Latent Representations for Controllable Speech Synthesis (2021.findings-acl)
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| Challenge: | Variational Auto-Encoders (VAEs) for learning disentangled latent representations in speech fail to learn latent clusters of speaker attributes when trained on limited or noisy datasets. |
| Approach: | They propose a Variational Auto-Encoder (VAE) that minimizes mutual information between latent variables and learns controllable latent representations in speech data. |
| Outcome: | The proposed model reduces the cluster overlap of speaker attributes by 30% over LSTM-VAE. |
On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond (2020.acl-main)
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| Challenge: | Existing work has shown that the optimization of variational autoencoders suffers from the posterior collapse problem. |
| Approach: | They propose a variational autoencoder that couples a VAE model with a deterministic autoencoding model and improves the parameters via weight sharing and decoder signal matching. |
| Outcome: | The proposed model improves on benchmark datasets and improves diversity of dialogue generation. |
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)
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Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan
| Challenge: | Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP). |
| Approach: | They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses. |
| Outcome: | The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets. |
Do sequence-to-sequence VAEs learn global features of sentences? (2020.emnlp-main)
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| Challenge: | Autoregressive language models are often trained without explicit conditioning labels . authors question claim that latent vectors can capture global features in unsupervised manner . |
| Approach: | They propose to use a sequence-to-sequence architecture to learn latent variables . they find that VAEs are prone to memorizing the first words and sentence length . |
| Outcome: | The proposed model is prone to memorizing the first words and sentence length, the authors show . et al., 2016: a new model learns latent variables that are more global, more predictive of topic or topic labels . authors question this claim, say it is a waste of time and money . |
A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text (D19-1)
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| Challenge: | Variational Autoencoders are powerful language models and effective representation learning frameworks. |
| Approach: | They propose a fix for posterior collapse which improves held-out likelihood, reconstruction and latent representation learning . |
| Outcome: | The proposed fix significantly improves held-out likelihood, reconstruction, and latent representation learning compared with previous state-of-the-art methods. |
Implicit Deep Latent Variable Models for Text Generation (D19-1)
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| Challenge: | Variational auto-encoders have been used for text generation but their representation power is limited due to two reasons. |
| Approach: | They advocate sample-based representations of variational distributions for natural language . they further develop an LVM to directly match the aggregated posterior to the prior . |
| Outcome: | The proposed model can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the "posterior collapse" issue. |
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (N19-1)
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| Challenge: | Experimental results show that the latent space learned by WAE exhibits properties of continuity and smoothness as in VAEs. |
| Approach: | They propose to use the variational autoencoder (VAE) for probabilistic sentence generation . they propose a variant of WAE that encourages the stochasticity of the encoder . |
| Outcome: | The proposed variant encourages the stochasticity of the encoder while achieving higher BLEU scores. |
Enhancing Variational Autoencoders with Mutual Information Neural Estimation for Text Generation (D19-1)
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| Challenge: | Existing approaches to train variational autoencoders (VAEs) have been proposed to alleviate the posterior collapse issue in NLP tasks. |
| Approach: | They propose to introduce a mutual information term between the input and its latent variable to regularize the objective of the VAE. |
| Outcome: | The proposed model performs better on three benchmark datasets and is comparable to state-of-the-art models. |
Spherical Latent Spaces for Stable Variational Autoencoders (D18-1)
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| Challenge: | Variational autoencoders use a multivariate Gaussian latent variable to capture latent structure in data. |
| Approach: | They propose a variational autoencoder which uses a latent distribution instead of Gaussian . they find that the variational posterior averts the KL collapse by a fixed hyperparameter . |
| Outcome: | The von Mises-Fisher distribution averts the KL collapse and gives better likelihoods than Gaussian models across a range of modeling conditions. |
Contrastive Deterministic Autoencoders For Language Modeling (2023.findings-emnlp)
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| Challenge: | Variational autoencoders (VAEs) are a popular family of generative models with wide applicability. |
| Approach: | They propose to modify a deterministic model designed for images to avoid posterior collapse by controlling the entropy of the aggregate posterior to make it Gaussian. |
| Outcome: | The proposed models outperform a broad range of VAE models on text generation and downstream tasks from representations while avoiding reparametrization steps. |
Generating Sentences from Disentangled Syntactic and Semantic Spaces (P19-1)
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| Challenge: | Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. |
| Approach: | They propose to generate sentences from disentangled syntactic and semantic spaces by using the linearized tree sequence. |
| Outcome: | The proposed method achieves similar or better performance in various tasks compared with state-of-the-art models. |