DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text (1802.01345v3)

Abstract: Existing text generation methods tend to produce repeated and "boring" expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward for repeatedly generated text and high reward for "novel" and fluent text, encouraging the generator to produce diverse and informative text. Moreover, we propose a novel language-model based discriminator, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators. The experimental results on review generation and dialogue generation tasks demonstrate that our model can generate substantially more diverse and informative text than existing baselines. The code is available at https://github.com/lancopku/DPGAN

An Analysis of Diversity-Promoting GAN for Text Generation

The paper "Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation" introduces a generative adversarial network model, referred to as DP-GAN, which aims to address the prevalent problem of low diversity in text generated by contemporary models. This work centers on the development of a novel GAN-based architecture that specifically rewards the generation of novel and fluent text, thus incentivizing diversity in text outputs.

Key Innovations

The proposed DP-GAN distinguishes itself with two primary innovations:

1. Incorporation of a Language-Model Based Discriminator: Traditional GANs for text generation typically employ a classifier-based discriminator, which can lead to reward saturation issues and a lack of precision in reward distribution between novel and less novel text. DP-GAN replaces this with a LLM that uses cross-entropy as a reward signal. This transition is argued to resolve the saturation issue and provide a more nuanced evaluation of novelty in generated text.
2. Novelty-Driven Reward Mechanism: The DP-GAN model redefines the reward structure by integrating both sentence-level and word-level rewards. This dual approach helps the model better encourage the generation of not just diverse sentences but also diverse n-graphic elements within those sentences.

Experimental Validation

The authors evaluate DP-GAN across two tasks: review generation and dialogue generation. The results demonstrate a substantial improvement over traditional baselines such as sequence-to-sequence models trained with maximum likelihood estimation (MLE) and existing GAN variations like SeqGAN.

• Diversity Metrics: The paper reports significant gains in diversity as measured by distinct n-gram counts. DP-GAN produces text with a broader vocabulary distribution and longer sentences, indicating a move away from repetitive and generic outputs that are symptomatic of MLE-trained models.
• Human Evaluation: A thorough human evaluation underscores the balance DP-GAN strikes between diversity, relevance, and fluency. Although the increased diversity occasionally affects fluency, the model's outputs are deemed more relevant and information-rich, particularly in comparison to baseline models.

Implications and Future Directions

The implications of this research are threefold. Practically, it provides a potentially improved framework for various NLP applications, such as dialogue systems and content creation, where diverse and coherent text generation is paramount. Theoretically, it enriches the GAN domain with a tailored approach to discriminator design and reward allocation, opening avenues for adaptations in other GAN-based tasks.

Looking forward, the adaptation of the DP-GAN framework to multilingual contexts or more complex tasks like story generation could further extend its utility. Additionally, optimizing the trade-off between diversity and the quality of generated text remains an open line of inquiry, hinting at the potential integration of adversarial and other learning paradigms, such as variational autoencoders, for more robust solutions.

In summary, this paper contributes a thoughtful and empirically validated method to enhance textual diversity in generated outputs, addressing a core challenge in automated text generation and setting the stage for subsequent advancements in generative capabilities of AI systems. The DP-GAN model signifies a step forward in encouraging more natural and varied forms of expression via machine-generated text.