DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text
By Jingjing Xu, Xu Sun, Xuancheng Ren, Junyang Lin, Bingzhen Wei, Wei Li. 2018
70 citations recorded by the source corpus.
Subfields: Generative Models, Natural Language Generation
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Generative Models
- Jianfeng Gao
- Aliaksei Severyn
- Greg Durrett
- Stanislau Semeniuta
- Erik Nijkamp
- Zhe Gan
- Sylvain Gelly
- Yizhe Zhang
In Natural Language Generation
Citations in the corpus
- Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization (2018, background): “Adversarial learning [6, 26] has been shown to be successful in dialog generation, translation, visual captioning and other natural language generation tasks [7, 12, 27, 28, 29, 30, 31, 32, 33, 34].”
- Synthesizing Tabular Data using Generative Adversarial Networks (2018, background): “However, generating discrete variables is a challenge for GANs: the authors in [26, 7] attempt a differential model by designing special functions or modifying the loss function; other researchers [50, 14, 46] use a reinforcement learning framework to train the non-differentiable model, making natur”
- Language GANs Falling Short (2018, methodology): “Research in both RL and non-RL GANs is still very active (e.g., Xu et al. (2018); Nie et al. (2019); d’Autume et al. (2019); Li et al. (2019); Gagnon-Marchand et al. (2019)) and so our review is not exhaustive.”
- On Accurate Evaluation of GANs for Language Generation (2018, methodology): “Previous work on GANs for text generation has used unique n-grams [31] and dataset-level BLEU scores [32, 14] .”
- Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation (2019, methodology): “During testing, we condition on the highest level of our similarity metric in order to generate maximally coherent responses (Xu et al., 2018b).”
- Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation (2020, background): “Fluency is often measured by a language model (Holtzman et al., 2018; Xu et al., 2018).”
- Detecting Language Model Attacks with Perplexity (2023, background): “They cite [Xu et al. 2018] who remark that "Fluency means that how likely the generated text is produced by human".”
- An Empirical Survey of Data Augmentation for Limited Data Learning in NLP (2021, methodology): “, 2018; (Xu et al., 2018), or pre-trained language models like GPT-2 (Anaby-Tavor et al.”