Language GANs Falling Short

TL;DR

This paper critically evaluates the effectiveness of GAN-based models for natural language generation, revealing that traditional maximum likelihood models often outperform GAN variants when considering quality and diversity, and highlighting flaws in current evaluation methods.

Contribution

The study challenges the prevailing belief that GANs are superior for NLG by demonstrating that MLE models outperform GAN variants across the entire quality-diversity spectrum and exposing flaws in standard evaluation metrics.

Findings

MLE models outperform GAN variants in quality and diversity.

Temperature tuning improves quality-diversity trade-offs more effectively than adversarial training.

Flaws in quality-only evaluation metrics can be exploited to artificially inflate performance.

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