MAUVE：使用Divergence Frontiers测量神经文本和人类文本之间的差距
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
摘要：
随着开放式文本生成的重大进展，衡量机器生成的文本与人类语言的接近程度仍然是一个关键的开放问题。我们介绍MAUVE，开放式文本生成的比较措施，它直接比较学习的分布从文本生成模型的人类书面文本的分布使用发散边界。MAUVE通过计算量化嵌入空间中的信息分歧来扩展到现代文本生成模型。通过对三个开放式生成任务的广泛实证研究，我们发现MAUVE识别生成文本的已知属性，随模型大小自然缩放，并与人类判断相关，比现有的分布式评估指标具有更少的限制。
英文摘要：
As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem. We introduce MAUVE, a comparison measure for open-ended text generation, which directly compares the learnt distribution from a text generation model to the distribution of human-written text using divergence frontiers. MAUVE scales up to modern text generation models by computing information divergences in a quantized embedding space. Through an extensive empirical study on three open-ended generation tasks, we find that MAUVE identifies known properties of generated text, scales naturally with model size, and correlates with human judgments, with fewer restrictions than existing distributional evaluation metrics.
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