Language GANs Falling Short (1811.02549v6)

Abstract: Generating high-quality text with sufficient diversity is essential for a wide range of Natural Language Generation (NLG) tasks. Maximum-Likelihood (MLE) models trained with teacher forcing have consistently been reported as weak baselines, where poor performance is attributed to exposure bias (Bengio et al., 2015; Ranzato et al., 2015); at inference time, the model is fed its own prediction instead of a ground-truth token, which can lead to accumulating errors and poor samples. This line of reasoning has led to an outbreak of adversarial based approaches for NLG, on the account that GANs do not suffer from exposure bias. In this work, we make several surprising observations which contradict common beliefs. First, we revisit the canonical evaluation framework for NLG, and point out fundamental flaws with quality-only evaluation: we show that one can outperform such metrics using a simple, well-known temperature parameter to artificially reduce the entropy of the model's conditional distributions. Second, we leverage the control over the quality / diversity trade-off given by this parameter to evaluate models over the whole quality-diversity spectrum and find MLE models constantly outperform the proposed GAN variants over the whole quality-diversity space. Our results have several implications: 1) The impact of exposure bias on sample quality is less severe than previously thought, 2) temperature tuning provides a better quality / diversity trade-off than adversarial training while being easier to train, easier to cross-validate, and less computationally expensive. Code to reproduce the experiments is available at github.com/pclucas14/GansFallingShort

An Analysis of "Language GANs Falling Short"

The paper "Language GANs Falling Short" presents a critical examination of the application of Generative Adversarial Networks (GANs) in natural language generation (NLG). In contrast to the initial hope that GANs might address some intrinsic issues of traditional LLMs, the paper illuminates significant shortcomings in these models compared to the established Maximum Likelihood Estimation (MLE) methods.

Key Insights and Contributions

1. Exposure Bias and Quality-Diversity Trade-off: The paper addresses the problem of exposure bias inherent in MLE-trained models, where the models are evaluated using samples from the model itself rather than ground-truth data during training. However, the paper posits that exposure bias might be less detrimental than the inefficiencies stemming from GAN-based training methodologies, especially considering the challenges of non-differentiable and sequential data often present in natural language.
2. GANs and MLE — A Comparative Evaluation: Through a rigorous experimental framework, the authors investigate and compare the sample quality generated by GAN-based models and MLE models. They argue that the benefits attributed to GANs — primarily the consistent training and inference procedures — are often overshadowed by the issues of sample diversity and mode collapse. This phenomenon reduces the effectiveness of GANs as they tend to generate limited variations of the data ("modes") rather than utilizing the full spectrum of possibilities.
3. Temperature Tuning as an Evaluation Tool: A significant methodological contribution of this paper is the introduction of 'temperature tuning' to adjust the softmax temperature during generation, thereby managing the trade-off between quality and diversity. This evaluation strategy provides a comprehensive framework for assessing NLG performance across both local and global metrics of quality and diversity. The results derived through temperature sweeping reveal that MLE models maintain superior performance across the entire quality-diversity spectrum.
4. GANs' Limitations: The empirical evidence presented demonstrates that well-parameterized MLE models can outperform GANs, which casts doubt on the latter's efficacy for certain NLG tasks. The paper explains that this is largely due to the computational complexities and the inadequacies in optimizing GANs for text data. This elucidates that GANs might fundamentally not provide the anticipated improvements over traditional techniques such as MLE.

Implications for Future Research

The findings from this paper suggest several implications for the future development and application of generative models in NLP:

• Benchmark and Evaluation Practices: There's a notable emphasis on refining evaluation metrics used in NLG research. This paper encourages moving beyond quality-only benchmarks, urging the community to consider both quality and diversity to get a robust assessment of generative models.
• Enhancing Model Training: The challenges associated with GANs highlight the need for novel training algorithms that can robustly handle the discrete and sequential nature of text generation tasks. Alternative paradigms might be necessary to circumvent the current limitations associated with the GAN framework.
• Theoretical Understanding: There should be a deeper theoretical exploration into why GAN training fails to effectively outperform MLE models in NLG tasks. Understanding the interplay between the generator and discriminator, particularly for discrete data, remains a critical research avenue.

Future Directions

As the paper provides a comprehensive critique of language GANs, it advocates for enhanced models or new directions that do not solely rely on adversarial training paradigms. For instance, improved reinforcement learning techniques, the application of novel architectures such as transformer-based models, or innovative regularization methodologies could potentially bridge the existing gap in performance. The insights provided in the paper serve as an important benchmark for evaluating generative models in NLP, setting clear expectations and limitations for adversarial methods in this domain.