GAN novice must read: how to apply GAN to NLP (thesis notes)

Author: MirandaYang Since its introduction, GANs have attracted significant attention, especially in the field of computer vision. "In-depth interpretation: GAN model and its progress in 2016" [1] provides a comprehensive overview of the developments in GANs over the past year, and it's highly recommended for those who are new to GANs. This article focuses on the application of GANs in NLP (which can be considered as a paper summary or note), and does not cover the basic concepts of GANs. If you're not familiar with GANs, I recommend reading [1] first because I'm not going to explain the basics here (J). Since it's been a while since I wrote in Chinese, please bear with me if there are any inaccuracies. Although GANs have achieved impressive results in image generation, they haven't made similar breakthroughs in natural language processing (NLP) tasks. The main reason is that GANs were originally designed for continuous data (real space), not discrete data like text. Dr. Ian Goodfellow, the creator of GAN theory, once explained this: “GANs are not currently applied to NLP. GANs are defined in the real domain, where the generator creates synthetic data, and the discriminator evaluates it. The gradient from the discriminator tells the generator how to make the generated data more realistic. However, this is only feasible when working with continuous data. For example, if you generate a pixel value of 1.0, you can slightly change it to 1.0001. But with text, you can't just modify a word like 'penguin' into 'penguin + 0.001', since such words don’t exist. NLP relies on discrete elements like words, letters, or syllables, making it challenging to apply GANs directly.” Another challenge lies in the nature of RNNs, which are often used for text generation. When generating long sequences, errors can accumulate exponentially, causing the quality of the generated text to degrade as the sentence length increases. Additionally, the length of the generated text is determined by the latent code, making it difficult to control the output length. Below, I will introduce and analyze some recent papers on applying GANs to NLP: 1. **Generating Text via Adversarial Training** Paper link: http://people.duke.edu/~yz196/pdf/textgan/paper.pdf This paper, presented at the 2016 NIPS GAN Workshop, attempts to apply GAN theory to text generation. The method uses an LSTM as the generator and applies smooth approximation to its outputs. The training process involves two steps: optimizing the discriminator and matching features. The generator is updated five times for every one update of the discriminator, due to the complexity of LSTM models. However, the model suffers from exposure bias during decoding. 2. **SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient** Paper link: https://arxiv.org/pdf/1609.05473.pdf Source: https://github.com/LantaoYu/SeqGAN This paper treats sequence generation as a sequential decision-making problem. It uses policy gradients to train the generator, with the discriminator’s output acting as a reward. The generator is an LSTM, and the discriminator is a CNN. The algorithm uses Monte Carlo search to estimate rewards for partially generated sequences. Experiments show improvements in tasks like poetry and speech generation. 3. **Adversarial Learning for Neural Dialogue Generation** Paper link: https://arxiv.org/pdf/1701.06547.pdf Source: https://github.com/jiweil/Neural-Dialogue-Generation This paper applies adversarial training to open-domain dialogue systems. It uses a seq2seq model as the generator and a historical encoder as the discriminator. The paper introduces a new method for calculating rewards for partially generated sequences and incorporates human responses to stabilize training. 4. **GANs for Sequence of Discrete Elements with the Gumbel-softmax Distribution** Paper link: https://arxiv.org/pdf/1611.04051.pdf This paper proposes a way to handle discrete data using the Gumbel-softmax trick, allowing differentiation through discrete sampling. Although the experiments are limited, the approach is promising for future research. 5. **Connecting Generative Adversarial Network and Actor-Critic Methods** Paper link: https://arxiv.org/pdf/1610.01945.pdf This paper draws parallels between GANs and actor-critic methods in reinforcement learning. Both involve a generator (actor) and a critic (discriminator), and the paper encourages collaboration between researchers in these fields. Overall, applying GANs to NLP remains a challenging but promising area of research. While early attempts faced difficulties with discrete data and sequence generation, newer approaches like SeqGAN and Gumbel-softmax offer potential solutions. As research continues, we may see more effective applications of GANs in text generation and other NLP tasks.

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