Information Value: Measuring Utterance Predictability as Distance from
  Plausible Alternatives
Mario Giulianelli Sarenne Wallbridge Raquel Fernández
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.