Adversarial Ranking for Language Generation (1705.11001v3)

Abstract: Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions. In this paper, we propose a novel generative adversarial network, RankGAN, for generating high-quality language descriptions. Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machine-written sentences by giving a reference group. By viewing a set of data samples collectively and evaluating their quality through relative ranking scores, the discriminator is able to make better assessment which in turn helps to learn a better generator. The proposed RankGAN is optimized through the policy gradient technique. Experimental results on multiple public datasets clearly demonstrate the effectiveness of the proposed approach.

Overview of Adversarial Ranking for Language Generation

The paper introduces RankGAN, a novel generative adversarial network designed to generate high-quality language descriptions through the use of adversarial ranking. Traditional GANs have been successful in data synthesis, particularly within the field of computer vision; however, their application to tasks involving complex and structured language generation has been limited. The existing models often restrict their discriminator to a binary classification framework, which inadequately captures the nuanced aspects of NLP. The authors propose RankGAN as a solution that utilizes ranking instead of binary prediction to improve the discriminator's ability to assess syntactic and semantic qualities in language generation tasks.

Unique Approach and Architectures

RankGAN distinguishes itself by employing a ranker in place of a conventional binary classifier. The ranker evaluates collections of sentences rather than individual ones, estimating relative ranking scores by comparing human-written and machine-generated sentences against a reference set. This shift from absolute classification to relative ranking allows the discriminator to provide richer, more nuanced feedback to the generator, enhancing the overall language generation quality. The generator is consequently optimized using policy gradient methods to navigate the non-differentiability issue inherent in discrete text sequences.

Results and Implications

The paper details extensive experiments demonstrating RankGAN’s superior performance across a suite of public datasets, including Chinese poetry, COCO image captions, and Shakespearean texts. RankGAN outperforms existing state-of-the-art methods, showing strong numerical results, such as improved BLEU scores on real-world language data. These results confirm the benefit of employing ranking-based discrimination over binary discrimination in the task of textual generation. Additionally, qualitative human evaluations further affirm the naturalness and fluency of sentences generated by RankGAN, hinting at its potential application in a plethora of NLP tasks.

Theoretical and Practical Implications

The proposed framework has significant theoretical ramifications, suggesting a novel direction for GAN applications in language-related tasks. By demonstrating effective learning through relative quality assessments, RankGAN offers a robust alternative for problems where discrete elements challenge traditional GAN models. Practically, this approach opens doors for advancements in machine translation, dialogue systems, and any domain requiring high-quality automatic text generation.

Future Developments in AI

This work marks a progression towards expanding GAN utility beyond image synthesis into more complex realms such as language. Future endeavors could entail extending the RankGAN framework to condition-based generation tasks, such as image captioning where context-specific language generation is required. Further, exploring the scalability of RankGAN in even larger datasets and more diversified languages can offer insights into its adaptability and potential improvements regarding speed and resource efficiency.

RankGAN represents a strategic step in advancing adversarial models for language generation tasks, showing promise for further explorations into more nuanced and context-sensitive language learning models within the AI landscape.