Essay on "Adversarial Generation of Natural Language"
The paper "Adversarial Generation of Natural Language" by Rajeswar et al. investigates the application of Generative Adversarial Networks (GANs) in the domain of natural language generation. While GANs have demonstrated significant success in image generation, transferring this success to NLP presents unique challenges, primarily due to the discrete nature of language data. This paper proposes a novel method to apply a GAN framework to generate plausible natural language sequences by addressing these intrinsic challenges.
The authors introduce a baseline approach to tackle the problem of discrete output spaces without depending on gradient estimators, which historically have been a source of numerous complications in GAN training for NLP. They explore both recurrent architectures (specifically LSTMs) and convolutional neural networks (CNNs) within the GAN framework and apply these networks to various datasets, evaluating the effectiveness of different GAN objectives, notably focusing on Wasserstein GANs (WGANs) and the incorporation of gradient penalties.
A key contribution of this work is the introduction of a discriminator that evaluates continuous-valued output distributions from the generator as opposed to discrete symbols. This innovation addresses the gradient flow issue arising from the non-differentiability of sampling operations in the discrete space. Additionally, the paper's exploration of sequence-level training, as opposed to traditional maximum likelihood training, aims to address exposure bias, a significant challenge in sequential data modeling that impairs the ability of models to generate coherent language.
The authors conducted experiments on datasets with varying complexity, including context-free grammars (CFGs), Chinese poetry, and large text corpora such as the Penn Treebank and the 1-billion-word dataset. Strong numerical results were obtained, demonstrating the proposed method's ability to generate grammatically plausible sentences and outperform existing adversarial methods on Chinese poetry generation datasets. Specifically, the LSTM architectures incorporating peephole connections alongside the WGAN-GP objective showed competitive BLEU scores against more complex methodologies like MaliGAN.
This paper underscores the potential of GANs in capturing the nuanced structure of human language when augmented with appropriate learning signals and architectural innovations. By convincingly demonstrating that GAN frameworks can be adapted to the discrete domain of language, the work implicitly suggests broader applications across various NLP tasks where traditional likelihood-based methods are prevalent. Furthermore, adversarial training, with GANs conditioning on high-level sentence attributes like sentiment and interrogative form, opens avenues for controllable language generation, which could significantly impact applications in conversational agents and content creation tools.
In sum, while this paper makes no claims to revolutionize the field, its methodical approach provides an important step towards leveraging GANs for the adversarial generation of natural language. Its findings suggest promising future research pathways, particularly the application of GAN-based sequence modeling in non-goal-oriented dialog systems and other sequential natural language tasks where conventional training and evaluation metrics are lacking. The integration of curriculum learning and exploration of different GAN objectives lay the groundwork for addressing the overlapping challenges of non-differentiability and effective sequence modeling within the larger scope of machine-generated text.