We present information value, a measure which quantifies the predictability of an utterance relative to a set of plausible alternatives. We introduce a method to obtain interpretable estimates of information value using neural text generators, and exploit their psychometric predictive power to investigate the dimensions of predictability that drive human comprehension behaviour. Information value is a stronger predictor of utterance acceptability in written and spoken dialogue than aggregates of token-level surprisal and it is complementary to surprisal for predicting eye-tracked reading times.

翻译：我们提出信息价值这一度量指标，用于量化话语相对于一组合理替代的可预测性。我们介绍了一种利用神经文本生成器获取信息价值可解释估计的方法，并利用其心理测量学预测能力来探究驱动人类理解行为的可预测性维度。在书面和口语对话中，信息价值比词元级惊奇度的聚合值更能预测话语可接受性，并且在预测眼动追踪阅读时间方面与惊奇度互补。