Neural Models of Selectional Preferences for Implicit Semantic Role Labeling

Implicit Semantic Role Labeling is a challenging task: it requires high-level understanding of the text while annotated data is very
limited. Due to the lack of training data, most researches either resort to simplistic machine learning methods or focus on automatically
acquiring training data. In this paper, we explore the possibilities of using more complex and expressive machine learning models
trained on a large amount of explicit roles. In addition, we compare the impact of one-way and multi-way selectional preference with the
hypothesis that the added information in multi-way models are beneficial. Although our models surpass a baseline that uses prototypical
vectors for SemEval-2010, we otherwise face mostly negative results. Selectional preference models perform lower than the baseline
on ON5V, a dataset of five ambiguous and frequent verbs. They are also outperformed by the Naive Bayes model of Feizabadi and
Pado (2015) on both datasets. We conclude that, even though multi-way selectional preference improves results for predicting explicit
semantic roles compared to one-way selectional preference, it harms performance for implicit roles. We release our source code,
including the reimplementation of two previously unavailable systems to enable further experimentation.