Analogous Process Structure Induction for Sub-event Sequence Prediction

Hongming Zhang, Muhao Chen, Haoyu Wang, Yangqiu Song, Dan Roth


Abstract
Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured representations of a sequence of events and on conceptualizing (abstracting) its components into (soft) event categories. Thus, knowledge about a known process such as “buying a car” can be used in the context of a new but analogous process such as “buying a house”. Nevertheless, most event understanding work in NLP is still at the ground level and does not consider abstraction. In this paper, we propose an Analogous Process Structure Induction (APSI) framework, which leverages analogies among processes and conceptualization of sub-event instances to predict the whole sub-event sequence of previously unseen open-domain processes. As our experiments and analysis indicate, APSI supports the generation of meaningful sub-event sequences for unseen processes and can help predict missing events.
Anthology ID:
2020.emnlp-main.119
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1541–1550
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.119
DOI:
10.18653/v1/2020.emnlp-main.119
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.119.pdf