Zhai, Fangzhou; Demberg, Vera; Koller, Alexander

Zero-shot Script Parsing

Proceedings of the 29th International Conference on Computational Linguistics, International Committee on Computational Linguistics, pp. 4049-4060, Gyeongju, Republic of Korea, 2022.

Script knowledge (Schank and Abelson, 1977) is useful for a variety of NLP tasks. However,
existing resources only cover 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 scenario-specific event and participant types, which enables us to acquire
script knowledge without domain-specific annotations. We (1) learn representations of po-
tential event and participant mentions by promoting class consistency according to the anno-
tated data; (2) perform clustering on the event /participant candidates from unannotated texts
that belongs to an unseen scenario. The model achieves 68.1/74.4 average F1 for event / par-
ticipant parsing, respectively, outperforming a previous CRF model that, in contrast, has ac-
cess to scenario-specific supervision. We also evaluate the model by testing on a different cor-
pus, where it achieved 55.5/54.0 average F1 for event / participant parsing.