Papers with CVAE
Diversifying Reply Suggestions Using a Matching-Conditional Variational Autoencoder (N19-2)
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| Challenge: | Automated reply suggestions (SR) are becoming common in many popular applications such as Gmail (2016) . |
| Approach: | They propose a constrained-sampling approach to make the variational inference efficient for a commercial instant-messaging system. |
| Outcome: | The proposed model increases diversity without losing relevance in offline experiments. |
Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction (D19-60)
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| Challenge: | Typical event sequences are important class of commonsense knowledge . previous work in event prediction uses sequence-to-sequence models . however, what can happen after a given event is usually diverse . |
| Approach: | They propose to incorporate a conditional variational autoencoder into seq2seq for its ability to represent diverse next events as a probabilistic distribution. |
| Outcome: | The proposed model outperforms deterministic models in terms of precision and recall . the proposed model is based on a conditional variational autoencoder . |
Incorporating Causal Analysis into Diversified and Logical Response Generation (2022.coling-1)
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| Challenge: | Existing generation-based models generate generic and safe responses such as "So am I" or "I don't know" |
| Approach: | They propose to predict the mediators to preserve relevant information and auto-regressively incorporate the mediator into generating process. |
| Outcome: | The proposed model generates relevant and informative responses and outperforms the state-of-the-art in terms of automatic metrics and human evaluations. |
Enhancing NER by Harnessing Multiple Datasets with Conditional Variational Autoencoders (2025.acl-short)
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task . supervised learning or full fine-tuning remains essential for high performance NER models. |
| Approach: | They propose to integrate CVAE into a span-based Named Entity Recognition model. |
| Outcome: | The proposed method achieves better performance on the BioRED dataset. |
CVAE-based Re-anchoring for Implicit Discourse Relation Classification (2021.findings-emnlp)
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| Challenge: | Existing studies show that training implicit discourse relation classifiers suffers from data sparsity. |
| Approach: | They propose a re-anchoring strategy to reduce the risk of erroneous sampling . they use Conditional VAE to estimate the risk and migrate the anchor to reduce it . |
| Outcome: | The proposed method improves the baseline classifier performance on PDTB v2.0 . |
Diversify Question Generation with Continuous Content Selectors and Question Type Modeling (2020.findings-emnlp)
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| Challenge: | Existing methods to generate questions based on answers and relevant contexts are not suitable for all questions . |
| Approach: | They propose a method to generate questions from a given answer and its relevant context. |
| Outcome: | The proposed method achieves a better trade-off between generation quality and diversity compared with existing approaches. |
A Discrete CVAE for Response Generation on Short-Text Conversation (D19-1)
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| Challenge: | Neural conversation models are easy to generate bland and generic responses . however, their improvement of generating high-quality responses is still unsatisfactory . |
| Approach: | They propose to use a discrete latent variable with an explicit semantic meaning to improve the conditional variational autoencoder on short-text conversation. |
| Outcome: | The proposed model outperforms various kinds of generation models under automatic and human evaluations and generates more diverse and informative responses. |
A Batch Normalized Inference Network Keeps the KL Vanishing Away (2020.acl-main)
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| Challenge: | Variational Autoencoder (VAE) is widely used to approximate a model’s posterior on latent variables. |
| Approach: | They propose to let the Kullback–Leibler divergence individual follow a distribution across the whole dataset and analyze that it is sufficient to prevent posterior collapse by keeping the expectation of the KL’s distribution positive. |
| Outcome: | The proposed approach can avoid posterior collapse effectively and efficiently without introducing any new model component or modifying the objective. |
Commonsense Justification for Action Explanation (D18-1)
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| Challenge: | a recent study examines the commonsense reasoning used by humans to justify an AI prediction. |
| Approach: | They propose an approach that models object relations/attributes of the world as latent variables and jointly learns a performer that predicts actions and an explainer that gathers commonsense evidence to justify the action. |
| Outcome: | The proposed model achieves significantly higher performance in both action prediction and justification. |
A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph (2020.findings-emnlp)
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| Challenge: | Topic-to-essay generation is a promising task for natural language generation. |
| Approach: | They propose a Sentiment Controllable topic-to- essay generator with a Topic Knowledge Graph enhanced decoder to generate essays with only several given topic words. |
| Outcome: | The proposed model outperforms the state-of-the-art model on automatic and human evaluation. |
Effective Data Augmentation for Sentence Classification Using One VAE per Class (2022.coling-1)
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| Challenge: | Variational auto-encoders and its conditional variant the Conditional-VAE (CVAE) are often used to generate new textual data, but they require more complex manipulations to ensure that the generated examples are useful. |
| Approach: | They propose a simple way to use Variational Auto-Encoders (VAE) for data augmentation by training one VAE per class. |
| Outcome: | The proposed method outperforms generative models on binary classification tasks and several dataset sizes on four different tasks. |
Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation (2023.findings-emnlp)
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| Challenge: | Existing variational dialog models have pre-trained, restricting diversity of responses . a diffusion model increases complexity of prior distribution and its compatibility with PLMs . |
| Approach: | They propose a hierarchical conditional variational autoencoder with diffusion priors to address these challenges. |
| Outcome: | The proposed method generates more diverse responses without dialog pre-training. |
Variational Autoregressive Decoder for Neural Response Generation (D18-1)
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| Challenge: | Existing variational Bayesian models generate responses from a single latent variable, which is not sufficient to model high variability in responses. |
| Approach: | They propose a conditional variable auto-encoder that sequentially introduces latent variables to condition the generation of each word in the response sequence. |
| Outcome: | Empirical results show that the proposed model improves on state-of-the-art models on Opensubtitle and Reddit datasets. |
Hierarchical CVAE for Fine-Grained Hate Speech Classification (D18-1)
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| Challenge: | Existing work on automated hate speech detection focuses on binary classification or on differentiating among a small set of categories. |
| Approach: | They propose a method to discriminate among 40 hate groups of 13 different hate group categories. |
| Outcome: | The proposed method outperforms discriminative models on a fine-grained hate speech classification task. |
Generating Relevant and Coherent Dialogue Responses using Self-Separated Conditional Variational AutoEncoders (2021.acl-long)
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| Challenge: | Conditional Variational AutoEncoders (CVAE) can enhance the diversity and informativeness of responses in open-domain dialogue generation tasks. |
| Approach: | They propose a Conditional Variational AutoEncoder (CVAE) that regularizes latent variables and introduces group information to regularize them. |
| Outcome: | Empirical results show that the proposed model can significantly boost responses in well-established open-domain dialogue datasets. |
From Attenuation to Attention: Variational Information Flow Manipulation for Fine-Grained Visual Perception (2026.findings-acl)
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| Challenge: | Existing input-centric solutions fail to reverse this intrinsic mechanism of information loss. |
| Approach: | They propose a Variational Information Flow framework that leverages a probabilistic perspective to model visual saliency relevant to the question-answer pair as a latent distribution. |
| Outcome: | The proposed framework improves general VQA, fine-grained perception and visual grounding. |