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Natural language generation (NLG) spans a broad range of tasks, each of whichserves for specific objectives and desires different properties of generatedtext. The complexity makes automatic evaluation of NLG particularlychallenging. Previous work has typically focused on a single task and developedindividual evaluation metrics based on specific intuitions. In this paper, wepropose a unifying perspective that facilitates the design of metrics for awide range of language generation tasks and quality aspects. Based on thenature of information change from input to output, we classify NLG tasks intocompression (e.g., summarization), transduction (e.g., text rewriting), andcreation (e.g., dialog). The information alignment, or overlap, between input,context, and output text plays a common central role in characterizing thegeneration. Using the uniform concept of information alignment, we develop afamily of interpretable metrics for various NLG tasks and aspects, oftenwithout need of gold reference data. To operationalize the metrics, we trainself-supervised models to approximate information alignment as a predictiontask. Experiments show the uniformly designed metrics achieve stronger orcomparable correlations with human judgement compared to state-of-the-artmetrics in each of diverse tasks, including text summarization, style transfer,and knowledge-grounded dialog. With information alignment as the intermediaterepresentation, we deliver a composable library for easy NLG evaluation andfuture metric design.

Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation