Abstract
Modeling script knowledge can be useful for
a wide range of NLP tasks. Current statistical
script learning approaches embed the events,
such that their relationships are indicated by
their similarity in the embedding. While intuitive, these approaches fall short of representing nuanced relations, needed for downstream tasks. In this paper, we suggest to view
learning event embedding as a multi-relational
problem, which allows us to capture different
aspects of event pairs. We model a rich set of
event relations, such as Cause and Contrast,
derived from the Penn Discourse Tree Bank.
We evaluate our model on three types of tasks,
the popular Mutli-Choice Narrative Cloze and
its variants, several multi-relational prediction
tasks, and a related downstream task—implicit
discourse sense classification