An Invariant Learning Characterization of Controlled Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Controlled generation is a problem of creating text that contains stylistic or semantic attributes of interest. |
| Approach: | They propose a distribution shift-based control system that can be used to train a predictor of the desired attribute. |
| Outcome: | The proposed method shows that the most effective predictor should be invariant across multiple text environments. |
Similar Papers
Attribute Alignment: Controlling Text Generation from Pre-trained Language Models (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models can generate text with sentiment polarity or specific topics without changing the original model parameters. |
| Approach: | They propose a method for controlling text generation by aligning disentangled attribute representations. |
| Outcome: | The proposed method shows large performance gains while maintaining diversity and fluency. |
A Distributional Lens for Multi-Aspect Controllable Text Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for multi-aspect control suffer from attribute degeneration due to mutual interference of these controllers. |
| Approach: | They propose to use attribute fusion to find the intersections of multiple attributes as their combination for generation. |
| Outcome: | The proposed method outperforms baselines on attribute relevance and text quality and achieves the SOTA. |
Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator (2022.coling-1)
Copied to clipboard
| Challenge: | Existing studies on controlled text generation focus on single-attribute control, but in practical applications, they lack controllability. |
| Approach: | They propose a framework for multi-attribute controlled text generation that can effectively generate texts with more attributes. |
| Outcome: | The proposed framework achieves remarkable controllability while keeping the text fluent and diverse. |
Exploring Controllable Text Generation Techniques (2020.coling-main)
Copied to clipboard
| Challenge: | Neural controllable text generation has a plethora of applications but there is no unifying theme. |
| Approach: | They propose a new schema for the control of attributes in the generation process by classifying it into five modules and providing an analysis on the advantages and disadvantages of these techniques. |
| Outcome: | The proposed frameworks can be used to control the attributes of natural sentences and to modulate the formality and politeness of emails. |
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing adversarial text generation approaches can lead to generation lacking diversity or fluency, whereas perturbing in the intermediate representation space can lead a model to generate generations that are not related to the input. |
| Approach: | They propose to generate adversarial texts through controllable attributes that are known to be invariant to task labels. |
| Outcome: | The proposed model generates more diverse and fluent adversarial examples, compared to existing approaches, and is more robust against model re-training and different model architectures. |
Stylized Text Generation: Approaches and Applications (2020.acl-tutorials)
Copied to clipboard
| Challenge: | Text generation has played an important role in various applications of natural language processing. |
| Approach: | They present different settings of stylized text generation and introduce machine learning methods to represent style. |
| Outcome: | This paper presents a comprehensive literature review on stylized text generation . it focuses on the challenges and future directions of stylized generation based on machine learning . |
Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning (2023.findings-acl)
Copied to clipboard
| Challenge: | Current language models have shown impressive capability of generating fluent and grammatical text, but they often produce behaviors misaligned with human expectations. |
| Approach: | They propose a new language model called Leo for controllable text generation which employs a contrastive loss on sequence likelihood which fundamentally decreases the generation probability of negative samples. |
| Outcome: | The proposed model outperforms baselines on language detoxification, sentiment steering, and repetition reduction tasks. |
Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work focuses on generating sentences satisfying pre-specified attributes such as topic and sentiment, yet suffers from increases in storage and inference time. |
| Approach: | They propose a method that uses a pre-trained continuous vector to generate a fixed pre-trainable language model to satisfy a specified attribute. |
| Outcome: | The proposed model can achieve improvements on eleven attribute-specific generation tasks with 0.08% extra training parameters. |
FUDGE: Controlled Text Generation With Future Discriminators (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent advances in large pretrained language models allow us to generate increasingly realistic text by modeling a distribution P (X) over natural language sequences X. |
| Approach: | They propose a flexible and modular method for controlled text generation that uses a Bayesian decomposition of the conditional distribution of G given an attribute predictor and can easily compose predictors for multiple desired attributes. |
| Outcome: | The proposed method can be easily composed and performs three tasks. |
Controlled Text Generation with Hidden Representation Transformations (2023.findings-acl)
Copied to clipboard
| Challenge: | Using a con-trolled language model, we gain attribute control by modifying the hidden representation of thebase model through learning transformations. |
| Approach: | They propose a con-trolled language generation framework that gains attribute control bymodifying the hidden representation of thebase model through learned transformations. |
| Outcome: | The proposed framework outperforms all thebaselines in detoxification, positivesentiment steering, and text simplification while minimizing the loss in linguistic qualities. |