Papers with disentangled representation learning
An Evaluation of Disentangled Representation Learning for Texts (2021.findings-acl)
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| 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. |
Separating Context and Pattern: Learning Disentangled Sentence Representations for Low-Resource Extractive Summarization (2023.findings-acl)
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| Challenge: | Context information is one of the key factors for extractive summarization, but other factors can be used to identify sentence importance. |
| Approach: | They propose to disentangle context and pattern factors for extractive summarization . they separate context and patterns for a better generalization ability in low-resource setting . |
| Outcome: | The proposed model can be used in the zero-shot setting or fine-tuned in the few-shot settings. |
Controllable Paraphrase Generation with a Syntactic Exemplar (P19-1)
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| Challenge: | Prior work on controllable text generation assumes that the generated attribute can take on a finite set of values known a priori. |
| Approach: | They propose a task where the syntax of a generated sentence is controlled rather by a sentential exemplar. |
| Outcome: | The proposed model achieves improvements over baselines and learns to capture desirable characteristics. |
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. |
| Outcome: | The proposed model outperforms baseline models in terms of data augmentation and text style transfer in the few-shot setting. |
N-CORE: N-View Consistency Regularization for Disentangled Representation Learning in Nonverbal Vocalizations (2025.emnlp-main)
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| Challenge: | Nonverbal vocalizations are an essential component of human communication, conveying rich information without linguistic content. |
| Approach: | They propose a backbone-agnostic framework to disentangle emotion and speaker information from nonverbal vocalizations by leveraging N views of audio samples to learn invariance to specific transformations. |
| Outcome: | The proposed framework achieves competitive performance compared to state-of-the-art methods on the VIVAE, ReCANVo, and ReCANVO-Balanced datasets. |