@inproceedings{chen-etal-2020-trying,
title = "What Are You Trying to Do? Semantic Typing of Event Processes",
author = "Chen, Muhao and
Zhang, Hongming and
Wang, Haoyu and
Roth, Dan",
editor = "Fern{\'a}ndez, Raquel and
Linzen, Tal",
booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.conll-1.43",
doi = "10.18653/v1/2020.conll-1.43",
pages = "531--542",
abstract = "This paper studies a new cognitively motivated semantic typing task,multi-axis event process typing, that, given anevent process, attempts to infer free-form typelabels describing (i) the type of action made bythe process and (ii) the type of object the pro-cess seeks to affect. This task is inspired bycomputational and cognitive studies of eventunderstanding, which suggest that understand-ing processes of events is often directed by rec-ognizing the goals, plans or intentions of theprotagonist(s). We develop a large dataset con-taining over 60k event processes, featuring ul-tra fine-grained typing on both the action andobject type axes with very large (10{\^{}}3∼10{\^{}}4)label vocabularies. We then propose a hybridlearning framework,P2GT, which addressesthe challenging typing problem with indirectsupervision from glosses1and a joint learning-to-rank framework. As our experiments indi-cate,P2GTsupports identifying the intent ofprocesses, as well as the fine semantic type ofthe affected object. It also demonstrates the ca-pability of handling few-shot cases, and stronggeneralizability on out-of-domain processes.",
}
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<abstract>This paper studies a new cognitively motivated semantic typing task,multi-axis event process typing, that, given anevent process, attempts to infer free-form typelabels describing (i) the type of action made bythe process and (ii) the type of object the pro-cess seeks to affect. This task is inspired bycomputational and cognitive studies of eventunderstanding, which suggest that understand-ing processes of events is often directed by rec-ognizing the goals, plans or intentions of theprotagonist(s). We develop a large dataset con-taining over 60k event processes, featuring ul-tra fine-grained typing on both the action andobject type axes with very large (10\³∼10\⁴)label vocabularies. We then propose a hybridlearning framework,P2GT, which addressesthe challenging typing problem with indirectsupervision from glosses1and a joint learning-to-rank framework. As our experiments indi-cate,P2GTsupports identifying the intent ofprocesses, as well as the fine semantic type ofthe affected object. It also demonstrates the ca-pability of handling few-shot cases, and stronggeneralizability on out-of-domain processes.</abstract>
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%0 Conference Proceedings
%T What Are You Trying to Do? Semantic Typing of Event Processes
%A Chen, Muhao
%A Zhang, Hongming
%A Wang, Haoyu
%A Roth, Dan
%Y Fernández, Raquel
%Y Linzen, Tal
%S Proceedings of the 24th Conference on Computational Natural Language Learning
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-trying
%X This paper studies a new cognitively motivated semantic typing task,multi-axis event process typing, that, given anevent process, attempts to infer free-form typelabels describing (i) the type of action made bythe process and (ii) the type of object the pro-cess seeks to affect. This task is inspired bycomputational and cognitive studies of eventunderstanding, which suggest that understand-ing processes of events is often directed by rec-ognizing the goals, plans or intentions of theprotagonist(s). We develop a large dataset con-taining over 60k event processes, featuring ul-tra fine-grained typing on both the action andobject type axes with very large (10\³∼10\⁴)label vocabularies. We then propose a hybridlearning framework,P2GT, which addressesthe challenging typing problem with indirectsupervision from glosses1and a joint learning-to-rank framework. As our experiments indi-cate,P2GTsupports identifying the intent ofprocesses, as well as the fine semantic type ofthe affected object. It also demonstrates the ca-pability of handling few-shot cases, and stronggeneralizability on out-of-domain processes.
%R 10.18653/v1/2020.conll-1.43
%U https://aclanthology.org/2020.conll-1.43
%U https://doi.org/10.18653/v1/2020.conll-1.43
%P 531-542
Markdown (Informal)
[What Are You Trying to Do? Semantic Typing of Event Processes](https://aclanthology.org/2020.conll-1.43) (Chen et al., CoNLL 2020)
ACL