Zero-Shot Script Parsing

Anonymous

Abstract: Script knowledge (shrank, 1977) proved useful to a variety of NLP tasks. However, existing resources only covering a small number of activities, limiting its practical usefulness. In this work, we propose a zero-shot learning approach to script parsing, the task of tagging texts with pre-defined, scenario-specific event and participant types, which makes it possible to acquire script knowledge without domain-specific annotations. We (1) learn representations of potential event and participant mentions by promoting cluster consistency according to the annotated data; (2) perform clustering on the event / participant candidates from unannotated texts that belongs to an unseen scenario. We further exploit dependency and coreference information. The model achieves 68.1/74.4 average F1 for event / participant parsing, respectively, outperforming a previous CRF model that has access to domain-specific supervision.