ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation
This addresses the need for better evaluation metrics in natural language generation, though it appears incremental by building on existing multimodal techniques.
The authors tackled the problem of automatic evaluation for natural language generation by proposing ImaginE, a metric that uses text-to-image generation to incorporate visual imagination, and found it improves correlation with human judgments across multiple tasks.
Automatic evaluations for natural language generation (NLG) conventionally rely on token-level or embedding-level comparisons with text references. This differs from human language processing, for which visual imagination often improves comprehension. In this work, we propose ImaginE, an imagination-based automatic evaluation metric for natural language generation. With the help of StableDiffusion, a state-of-the-art text-to-image generator, we automatically generate an image as the embodied imagination for the text snippet and compute the imagination similarity using contextual embeddings. Experiments spanning several text generation tasks demonstrate that adding machine-generated images with our ImaginE displays great potential in introducing multi-modal information into NLG evaluation, and improves existing automatic metrics' correlations with human similarity judgments in both reference-based and reference-free evaluation scenarios.