# Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction

arXiv: https://arxiv.org/abs/2306.00418 · HTML: https://mlsanity.com/paper/2306.00418/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 22 of 22)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Neural Text Generation with Unlikelihood Training](https://mlsanity.com/paper/1908.04319/) (2019)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training](https://mlsanity.com/paper/1911.03860/) (2019)
- [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://mlsanity.com/paper/2110.00796/) (2021)
- [Position-Aware Tagging for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2010.02609/) (2020)
- [BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data](https://mlsanity.com/paper/2106.06169/) (2021)
- [MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models](https://mlsanity.com/paper/2010.00840/) (2020)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [Distributional Semantics and Linguistic Theory](https://mlsanity.com/paper/1905.01896/) (2019)
- [Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction](https://mlsanity.com/paper/1805.04601/) (2018)
- [Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation](https://mlsanity.com/paper/2210.10291/) (2022)
- [Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification](https://mlsanity.com/paper/1909.06276/) (2019)
- [CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis](https://mlsanity.com/paper/1812.10735/) (2018)
- [ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction](https://mlsanity.com/paper/2103.06605/) (2021)
- [A Survey on Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2203.14266/) (2022)
- [Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive Structure](https://mlsanity.com/paper/2211.07743/) (2022)

## Built on by (most cited first, 11 of 11)

- [MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2306.16956/) (2023)
- [BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2406.07365/) (2024)
- [Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2406.18078/) (2024)
- [Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding](https://mlsanity.com/paper/2310.11191/) (2023)
- [Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning](https://mlsanity.com/paper/2403.14399/) (2024)
- [iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples](https://mlsanity.com/paper/2311.03896/) (2023)
- [STAR: Stepwise Task Augmentation with Relation Learning for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2501.16093/) (2025)
- [Adaptive Data Augmentation for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2401.06394/) (2024)
- [Listwise Preference Optimization with Element-wise Confusions for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2511.23184/) (2025)
- [Generate Then Correct: Single Shot Global Correction for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2603.13777/) (2026)
- [Dependency Reasoning for Aspect Sentiment Quadruple Prediction](https://mlsanity.com/paper/2610.14873/) (2026)

## Abstract (arXiv)

Recently, aspect sentiment quad prediction has received widespread attention in the field of aspect-based sentiment analysis. Existing studies extract quadruplets via pre-trained generative language models to paraphrase the original sentence into a templated target sequence. However, previous works only focus on what to generate but ignore what not to generate. We argue that considering the negative samples also leads to potential benefits. In this work, we propose a template-agnostic method to control the token-level generation, which boosts original learning and reduces mistakes simultaneously. Specifically, we introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models, acquiring the noises and errors. We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens. Finally, we introduce minimization entropy to balance the effects of marginalized unlikelihood learning. Extensive experiments on four public datasets demonstrate the effectiveness of our approach on various generation templates.