| 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. |
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| 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 Semantic Spaces of Explanations via Invertible Neural Networks (2024.acl-long)
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| Challenge: | Disentangled latent spaces usually have better semantic separability and geometrical properties, which leads to better interpretability and controllable data generation. |
| Approach: | They propose a flow-based invertible neural network mechanism integrated with a transformer-based language Autoencoder to deliver latent spaces with better semantic separability and geometrical properties. |
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Syntax-Infused Variational Autoencoder for Text Generation (P19-1)
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| Challenge: | Experimental results demonstrate the generative superiority of SIVAE on both reconstruction and targeted syntactic evaluations. |
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Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space (2020.emnlp-main)
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| Challenge: | Existing models for language understanding and understanding can be trained to provide contextualized representations of words based on text data. |
| Approach: | They propose a large-scale language VAE model Optimus that is pre-trained on large text corpus and fine-tuned for various language generation and understanding tasks. |
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Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation (2022.emnlp-main)
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| 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. |
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A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations (N19-1)
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| Challenge: | Empirically, the model with the best performing syntactic and semantic representations gives rise to the most disentangled representations. |
| Approach: | They propose a generative model that uses latent variables to learn a sentence that uses both latent and latent representations. |
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A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation (2021.findings-emnlp)
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| Challenge: | Recent advances in disentanglement work on coarse levels in the disenanglement of closely related properties, such as syntax and semantics in human languages. |
| Approach: | They propose a deep decomposable model based on VAE to disentangle syntax and semantics by using total correlation penalties on KL divergences. |
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A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation (2020.coling-main)
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| Challenge: | Paraphrase generation is a longstanding problem in natural language processing (NLP) Neural network-based methods have shown great progress on paraphrase generation. |
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Empirical Prior for Text Autoencoders (2024.findings-emnlp)
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| 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. |
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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. |
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