Adversarial Text Generation via Sequence Contrast Discrimination (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to generate human-like texts are auto-regressive, but they suffer from exposure bias due to the dependence on the previous sampled output during the inferring phase. |
| Approach: | They propose a sequence contrast loss driven text generation framework which learns the difference between real texts and generated texts and uses that difference. |
| Outcome: | The proposed framework improves training stability and quality of generated texts and avoids the time-consuming sampling process. |
Similar Papers
CoCGAN: Contrastive Learning for Adversarial Category Text Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Experimental results on synthetic and real category text generation datasets demonstrate that CoCGAN can achieve significant improvements over the baseline category text generators. |
| Approach: | They propose to incorporate contrastive learning into adversarial category text generation by using a discriminator to optimize a contrastive learn objective to capture more flexible data-to-class relations and data- to-data relations among training samples. |
| Outcome: | The proposed model improves on synthetic and real category text generation datasets. |
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation (N19-1)
Copied to clipboard
| Challenge: | Text generation with generative adversarial networks (GANs) can be divided into text-based and code-based categories depending on the type of signals used for discrimination. |
| Approach: | They propose a text-based approach to exploit generative adversarial networks (GANs) by using autoencoders to provide a continuous representation of sentences, which they will refer to as soft-text, and hybrid latent code and text-oriented approaches with one or more discriminators. |
| Outcome: | The proposed approach outperforms the traditional GAN-based methods on two well-known datasets. |
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent work has shown that models can be easily fooled by intentionally designed adversarial examples. |
| Approach: | They propose two efficient approaches for generating adversarial perturbations on embeddings and propose two new approaches to help model learn adversarials more efficiently. |
| Outcome: | The proposed approaches outperform strong baselines on various text classification datasets and the model's performance drops less under adversarial attack. |
Learning with Contrastive Examples for Data-to-Text Generation (2020.coling-main)
Copied to clipboard
Yui Uehara, Tatsuya Ishigaki, Kasumi Aoki, Hiroshi Noji, Keiichi Goshima, Ichiro Kobayashi, Hiroya Takamura, Yusuke Miyao
| Challenge: | Existing models for data-to-text generation generate fluent but sometimes incorrect sentences . Existing studies show that using contrastive examples improves the ability of generating sentences with better lexical choice without degrading the fluency. |
| Approach: | They propose to use models trained on incorrect sentences and learning methods that exploit contrastive examples to reduce such errors. |
| Outcome: | The proposed models generate fluent sentences but often have problematic ones in terms of correctness. |
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. |
GLTR: Statistical Detection and Visualization of Generated Text (P19-3)
Copied to clipboard
| Challenge: | GLTR is a tool to detect generated text that can be used by non-experts. |
| Approach: | They propose a tool to detect generated text using a set of statistical methods that can be used by non-experts. |
| Outcome: | The proposed method improves detection rate of fake text from 54% to 72% without training. |
Detecting Machine-Generated Text: Techniques and Challenges (2024.acl-tutorials)
Copied to clipboard
| Challenge: | This tutorial focuses on machine-generated text and deepfakes. |
| Approach: | This tutorial aims to provide a comprehensive overview of text detection techniques . it will focus on machine-generated text and deepfakes . |
| Outcome: | This tutorial focuses on machine-generated text and deepfakes. |
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation (D18-1)
Copied to clipboard
| Challenge: | Existing text generation methods tend to produce repeated and ”boring” expressions. |
| Approach: | They propose a model that assigns low reward for repeatedly generated text and high reward for ”novel” and fluent text, and a novel language-model based discriminator which can distinguish novel text from repeated text without the saturation problem. |
| Outcome: | The proposed model generates more diverse and informative text than existing baselines on review generation and dialogue generation tasks. |
Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing approaches to language modeling use autoregressive methods, but they can produce repetitive results. |
| Approach: | They propose to add a loss function for regularization to avoid unwanted properties, such as contradiction or repetition, to a language model by using policy gradient reinforcement learning. |
| Outcome: | The proposed method reduces repetition without impacting the language model quality. |
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 . |