# Learning Disentangled Representations of Negation and Uncertainty

arXiv: https://arxiv.org/abs/2204.00511 · HTML: https://mlsanity.com/paper/2204.00511/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 63 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information](https://mlsanity.com/paper/2006.12013/) (2020)
- [Isolating Sources of Disentanglement in Variational Autoencoders](https://mlsanity.com/paper/1802.04942/) (2018)
- [Style Transfer in Text: Exploration and Evaluation](https://mlsanity.com/paper/1711.06861/) (2017)
- [Disentangled Representation Learning for Non-Parallel Text Style Transfer](https://mlsanity.com/paper/1808.04339/) (2018)
- [Learning Disentangled Joint Continuous and Discrete Representations](https://mlsanity.com/paper/1804.00104/) (2018)
- [Weakly Supervised Disentanglement with Guarantees](https://mlsanity.com/paper/1910.09772/) (2019)
- [Generating Sentences from Disentangled Syntactic and Semantic Spaces](https://mlsanity.com/paper/1907.05789/) (2019)
- [Improving Disentangled Text Representation Learning with Information-Theoretic Guidance](https://mlsanity.com/paper/2006.00693/) (2020)
- [Content preserving text generation with attribute controls](https://mlsanity.com/paper/1811.01135/) (2018)
- [A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations](https://mlsanity.com/paper/2105.02685/) (2021)
- [NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution](https://mlsanity.com/paper/1911.04211/) (2019)
- [A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC](https://mlsanity.com/paper/1809.10735/) (2018)
- [Weakly Supervised Disentanglement by Pairwise Similarities](https://mlsanity.com/paper/1906.01044/) (2019)
- [Improving Sentiment Analysis with Multi-task Learning of Negation](https://mlsanity.com/paper/1906.07610/) (2019)
- [Exploring Different Dimensions of Attention for Uncertainty Detection](https://mlsanity.com/paper/1612.06549/) (2016)
- [Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation](https://mlsanity.com/paper/2004.10809/) (2020)
- [Do sequence-to-sequence VAEs learn global features of sentences?](https://mlsanity.com/paper/2004.07683/) (2020)
- [Seeing Through Misinformation: A Framework for Identifying Fake Online News](https://mlsanity.com/paper/1804.03508/) (2018)

## Built on by (most cited first, 13 of 13)

- [Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks](https://mlsanity.com/paper/2305.01713/) (2023)
- [Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors](https://mlsanity.com/paper/2305.17055/) (2023)
- [Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders](https://mlsanity.com/paper/2402.00723/) (2024)
- [Quasi-symbolic Semantic Geometry over Transformer-based Variational AutoEncoder](https://mlsanity.com/paper/2210.06230/) (2022)
- [Disentangled Variational Autoencoder for Emotion Recognition in Conversations](https://mlsanity.com/paper/2305.14071/) (2023)
- [Characterizing Mamba's Selective Memory using Auto-Encoders](https://mlsanity.com/paper/2512.15653/) (2025)
- [LangVAE and LangSpace: Building and Probing for Language Model VAEs](https://mlsanity.com/paper/2505.00004/) (2025)
- [Object Recognition from Scientific Document based on Compartment Refinement Framework](https://mlsanity.com/paper/2312.09038/) (2023)
- [Logical Negation Augmenting and Debiasing for Prompt-based Methods](https://mlsanity.com/paper/2405.04872/) (2024)
- [CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing](https://mlsanity.com/paper/2305.02865/) (2023)
- [Factuality Detection using Machine Translation -- a Use Case for German Clinical Text](https://mlsanity.com/paper/2308.08827/) (2023)
- [Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncoder](https://mlsanity.com/paper/2506.20083/) (2025)
- [Uncovering Latent Reasoning Strategies in Language Models](https://mlsanity.com/paper/2607.17674/) (2026)

## Abstract (arXiv)

Negation and uncertainty modeling are long-standing tasks in natural language processing. Linguistic theory postulates that expressions of negation and uncertainty are semantically independent from each other and the content they modify. However, previous works on representation learning do not explicitly model this independence. We therefore attempt to disentangle the representations of negation, uncertainty, and content using a Variational Autoencoder. We find that simply supervising the latent representations results in good disentanglement, but auxiliary objectives based on adversarial learning and mutual information minimization can provide additional disentanglement gains.